Overview

Brought to you by YData

Dataset statistics

Number of variables58
Number of observations263
Missing cells40
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory311.9 KiB
Average record size in memory1.2 KiB

Variable types

Numeric32
Text1
Categorical21
Unsupported4

Alerts

Total_Credits has constant value "19.0"Constant
BEE_Credit is highly overall correlated with BEE_ESE and 24 other fieldsHigh correlation
BEE_ESE is highly overall correlated with BEE_Credit and 15 other fieldsHigh correlation
BEE_GPA is highly overall correlated with BEE_Credit and 18 other fieldsHigh correlation
BEE_GPC is highly overall correlated with BEE_Credit and 18 other fieldsHigh correlation
BEE_Grade is highly overall correlated with BEE_Credit and 25 other fieldsHigh correlation
BEE_Status is highly overall correlated with BEE_Credit and 25 other fieldsHigh correlation
BEE_Total is highly overall correlated with BEE_Credit and 23 other fieldsHigh correlation
EG_Credit is highly overall correlated with BEE_Grade and 30 other fieldsHigh correlation
EG_ESE is highly overall correlated with EG_Credit and 9 other fieldsHigh correlation
EG_GPA is highly overall correlated with BEE_Grade and 20 other fieldsHigh correlation
EG_GPC is highly overall correlated with BEE_Grade and 22 other fieldsHigh correlation
EG_Total is highly overall correlated with BEE_Credit and 13 other fieldsHigh correlation
EM_Credit is highly overall correlated with EG_Credit and 8 other fieldsHigh correlation
EM_ESE is highly overall correlated with EG_Credit and 8 other fieldsHigh correlation
EM_GPA is highly overall correlated with EG_Credit and 8 other fieldsHigh correlation
EM_GPC is highly overall correlated with EG_Credit and 8 other fieldsHigh correlation
EM_Total is highly overall correlated with EG_Credit and 10 other fieldsHigh correlation
LLC_Credit is highly overall correlated with LLC_GPA and 3 other fieldsHigh correlation
LLC_GPA is highly overall correlated with LLC_Credit and 3 other fieldsHigh correlation
LLC_GPC is highly overall correlated with LLC_Credit and 3 other fieldsHigh correlation
LLC_Grade is highly overall correlated with EG_Credit and 17 other fieldsHigh correlation
LLC_Total is highly overall correlated with LLC_Credit and 5 other fieldsHigh correlation
Maths_Credit is highly overall correlated with BEE_Credit and 33 other fieldsHigh correlation
Maths_ESE is highly overall correlated with BEE_Credit and 17 other fieldsHigh correlation
Maths_GPA is highly overall correlated with BEE_Credit and 29 other fieldsHigh correlation
Maths_GPC is highly overall correlated with BEE_Credit and 16 other fieldsHigh correlation
Maths_Grade is highly overall correlated with BEE_Credit and 24 other fieldsHigh correlation
Maths_Status is highly overall correlated with BEE_Credit and 29 other fieldsHigh correlation
Maths_Total is highly overall correlated with BEE_Credit and 19 other fieldsHigh correlation
PSOOP_Credit is highly overall correlated with BEE_Grade and 29 other fieldsHigh correlation
PSOOP_GPA is highly overall correlated with BEE_ESE and 35 other fieldsHigh correlation
PSOOP_GPC is highly overall correlated with BEE_ESE and 35 other fieldsHigh correlation
PSOOP_Grade is highly overall correlated with BEE_Credit and 23 other fieldsHigh correlation
PSOOP_Status is highly overall correlated with BEE_Grade and 29 other fieldsHigh correlation
PSOOP_Total is highly overall correlated with BEE_Credit and 25 other fieldsHigh correlation
Result is highly overall correlated with BEE_Credit and 25 other fieldsHigh correlation
SGPA is highly overall correlated with BEE_Credit and 22 other fieldsHigh correlation
SS_Credit is highly overall correlated with BEE_Grade and 25 other fieldsHigh correlation
SS_GPA is highly overall correlated with BEE_Credit and 25 other fieldsHigh correlation
SS_GPC is highly overall correlated with BEE_GPA and 28 other fieldsHigh correlation
SS_Grade is highly overall correlated with BEE_Credit and 22 other fieldsHigh correlation
SS_Status is highly overall correlated with BEE_Grade and 25 other fieldsHigh correlation
SS_Total is highly overall correlated with BEE_Credit and 19 other fieldsHigh correlation
Total_Credits_Earned is highly overall correlated with BEE_Credit and 16 other fieldsHigh correlation
Total_GPC_Earned is highly overall correlated with BEE_Credit and 21 other fieldsHigh correlation
UHV_Credit is highly overall correlated with BEE_Grade and 25 other fieldsHigh correlation
UHV_GPA is highly overall correlated with BEE_Grade and 25 other fieldsHigh correlation
UHV_GPC is highly overall correlated with BEE_Grade and 24 other fieldsHigh correlation
UHV_Grade is highly overall correlated with BEE_Credit and 18 other fieldsHigh correlation
UHV_Status is highly overall correlated with BEE_Grade and 24 other fieldsHigh correlation
UHV_Total is highly overall correlated with BEE_Credit and 17 other fieldsHigh correlation
Maths_Status is highly imbalanced (73.6%)Imbalance
Maths_Credit is highly imbalanced (66.2%)Imbalance
BEE_Status is highly imbalanced (68.5%)Imbalance
SS_Status is highly imbalanced (93.7%)Imbalance
SS_Credit is highly imbalanced (93.7%)Imbalance
PSOOP_Status is highly imbalanced (96.4%)Imbalance
PSOOP_Credit is highly imbalanced (96.4%)Imbalance
UHV_Status is highly imbalanced (94.6%)Imbalance
UHV_Credit is highly imbalanced (95.5%)Imbalance
LLC_Credit is highly imbalanced (71.6%)Imbalance
Maths_ESE has 4 (1.5%) missing valuesMissing
BEE_ESE has 5 (1.9%) missing valuesMissing
EG_ESE has 4 (1.5%) missing valuesMissing
LLC_Total has 10 (3.8%) missing valuesMissing
LLC_Grade has 6 (2.3%) missing valuesMissing
UID has unique valuesUnique
Name has unique valuesUnique
EM_Status is an unsupported type, check if it needs cleaning or further analysisUnsupported
EM_Grade is an unsupported type, check if it needs cleaning or further analysisUnsupported
EG_Status is an unsupported type, check if it needs cleaning or further analysisUnsupported
EG_Grade is an unsupported type, check if it needs cleaning or further analysisUnsupported
Maths_GPA has 29 (11.0%) zerosZeros
Maths_GPC has 29 (11.0%) zerosZeros
BEE_GPA has 43 (16.3%) zerosZeros
BEE_GPC has 43 (16.3%) zerosZeros
EM_ESE has 134 (51.0%) zerosZeros
EM_Total has 133 (50.6%) zerosZeros
EM_GPA has 146 (55.5%) zerosZeros
EM_GPC has 145 (55.1%) zerosZeros
EG_ESE has 130 (49.4%) zerosZeros
EG_Total has 130 (49.4%) zerosZeros
EG_GPA has 151 (57.4%) zerosZeros
EG_GPC has 151 (57.4%) zerosZeros
LLC_GPA has 13 (4.9%) zerosZeros
LLC_GPC has 13 (4.9%) zerosZeros

Reproduction

Analysis started2024-10-03 20:43:36.769999
Analysis finished2024-10-03 20:45:20.466458
Duration1 minute and 43.7 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

UID
Real number (ℝ)

UNIQUE 

Distinct263
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0232887 × 109
Minimum2.0203 × 109
Maximum2.0233003 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:20.569722image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2.0203 × 109
5-th percentile2.0233 × 109
Q12.0233001 × 109
median2.0233001 × 109
Q32.0233002 × 109
95-th percentile2.0233003 × 109
Maximum2.0233003 × 109
Range3000225
Interquartile range (IQR)132

Descriptive statistics

Standard deviation184993.61
Coefficient of variation (CV)9.1432134 × 10-5
Kurtosis262.99991
Mean2.0232887 × 109
Median Absolute Deviation (MAD)66
Skewness-16.217271
Sum5.3212493 × 1011
Variance3.4222634 × 1010
MonotonicityNot monotonic
2024-10-04T02:15:20.703926image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020300041 1
 
0.4%
2023300001 1
 
0.4%
2023300002 1
 
0.4%
2023300003 1
 
0.4%
2023300005 1
 
0.4%
2023300006 1
 
0.4%
2023300007 1
 
0.4%
2023300249 1
 
0.4%
2023300248 1
 
0.4%
2023300247 1
 
0.4%
Other values (253) 253
96.2%
ValueCountFrequency (%)
2020300041 1
0.4%
2023300001 1
0.4%
2023300002 1
0.4%
2023300003 1
0.4%
2023300005 1
0.4%
2023300006 1
0.4%
2023300007 1
0.4%
2023300008 1
0.4%
2023300009 1
0.4%
2023300010 1
0.4%
ValueCountFrequency (%)
2023300266 1
0.4%
2023300265 1
0.4%
2023300264 1
0.4%
2023300263 1
0.4%
2023300260 1
0.4%
2023300259 1
0.4%
2023300258 1
0.4%
2023300257 1
0.4%
2023300256 1
0.4%
2023300255 1
0.4%

Name
Text

UNIQUE 

Distinct263
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size19.8 KiB
2024-10-04T02:15:20.910917image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length30
Median length26
Mean length20.144487
Min length10

Characters and Unicode

Total characters5298
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique263 ?
Unique (%)100.0%

Sample

1st rowAARYAN MANTRI
2nd rowAGARWAL VEDANT RAKESH
3rd rowKRISH PRAFULKUMAR AGRAWAL
4th rowANGADI ARAN AJIT
5th rowATOLE ANIKET SAYAJI
ValueCountFrequency (%)
shah 21
 
2.7%
jadhav 7
 
0.9%
santosh 6
 
0.8%
soham 6
 
0.8%
yash 6
 
0.8%
jain 5
 
0.6%
mehta 5
 
0.6%
manish 5
 
0.6%
rahul 5
 
0.6%
aryan 5
 
0.6%
Other values (563) 709
90.9%
2024-10-04T02:15:21.319690image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 944
17.8%
517
9.8%
H 492
 
9.3%
I 384
 
7.2%
S 351
 
6.6%
R 347
 
6.5%
N 315
 
5.9%
E 272
 
5.1%
T 201
 
3.8%
D 193
 
3.6%
Other values (24) 1282
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4770
90.0%
Space Separator 517
 
9.8%
Lowercase Letter 11
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 944
19.8%
H 492
10.3%
I 384
 
8.1%
S 351
 
7.4%
R 347
 
7.3%
N 315
 
6.6%
E 272
 
5.7%
T 201
 
4.2%
D 193
 
4.0%
K 180
 
3.8%
Other values (16) 1091
22.9%
Lowercase Letter
ValueCountFrequency (%)
a 3
27.3%
s 2
18.2%
r 2
18.2%
e 1
 
9.1%
i 1
 
9.1%
l 1
 
9.1%
h 1
 
9.1%
Space Separator
ValueCountFrequency (%)
517
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4781
90.2%
Common 517
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 944
19.7%
H 492
10.3%
I 384
 
8.0%
S 351
 
7.3%
R 347
 
7.3%
N 315
 
6.6%
E 272
 
5.7%
T 201
 
4.2%
D 193
 
4.0%
K 180
 
3.8%
Other values (23) 1102
23.0%
Common
ValueCountFrequency (%)
517
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 944
17.8%
517
9.8%
H 492
 
9.3%
I 384
 
7.2%
S 351
 
6.6%
R 347
 
6.5%
N 315
 
5.9%
E 272
 
5.1%
T 201
 
3.8%
D 193
 
3.6%
Other values (24) 1282
24.2%

Maths_ESE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)32.4%
Missing4
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean64.986486
Minimum0
Maximum99
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:21.450723image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.7
Q152
median71
Q383
95-th percentile93.1
Maximum99
Range99
Interquartile range (IQR)31

Descriptive statistics

Standard deviation23.685005
Coefficient of variation (CV)0.36446047
Kurtosis0.075458601
Mean64.986486
Median Absolute Deviation (MAD)14
Skewness-0.91532649
Sum16831.5
Variance560.97947
MonotonicityNot monotonic
2024-10-04T02:15:21.549886image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 10
 
3.8%
80 8
 
3.0%
76 8
 
3.0%
63 8
 
3.0%
83 7
 
2.7%
84 7
 
2.7%
82 6
 
2.3%
69 6
 
2.3%
88 6
 
2.3%
72 6
 
2.3%
Other values (74) 187
71.1%
ValueCountFrequency (%)
0 1
0.4%
1 1
0.4%
2 1
0.4%
4 2
0.8%
6 1
0.4%
8 1
0.4%
10 2
0.8%
11 1
0.4%
12 2
0.8%
13 1
0.4%
ValueCountFrequency (%)
99 2
 
0.8%
98 2
 
0.8%
97 1
 
0.4%
96 2
 
0.8%
95 1
 
0.4%
94 5
1.9%
93 5
1.9%
92 4
1.5%
91 2
 
0.8%
90 3
1.1%

Maths_Total
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)28.6%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean65.576336
Minimum4
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:21.641555image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile19
Q156
median71
Q382
95-th percentile92
Maximum96
Range92
Interquartile range (IQR)26

Descriptive statistics

Standard deviation22.41903
Coefficient of variation (CV)0.34187684
Kurtosis0.35900533
Mean65.576336
Median Absolute Deviation (MAD)12
Skewness-1.0244012
Sum17181
Variance502.61292
MonotonicityNot monotonic
2024-10-04T02:15:21.781220image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 12
 
4.6%
82 9
 
3.4%
67 9
 
3.4%
79 8
 
3.0%
69 8
 
3.0%
74 8
 
3.0%
80 8
 
3.0%
90 7
 
2.7%
88 7
 
2.7%
83 7
 
2.7%
Other values (65) 179
68.1%
ValueCountFrequency (%)
4 2
0.8%
5 2
0.8%
6 1
0.4%
7 1
0.4%
8 1
0.4%
11 2
0.8%
12 2
0.8%
13 1
0.4%
17 1
0.4%
19 2
0.8%
ValueCountFrequency (%)
96 2
 
0.8%
95 2
 
0.8%
94 2
 
0.8%
93 5
1.9%
92 4
1.5%
91 7
2.7%
90 7
2.7%
89 2
 
0.8%
88 7
2.7%
87 1
 
0.4%

Maths_Status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size15.9 KiB
pass
233 
fail in ESE
25 
absent in ESE
 
3
Detained,att endance shortage
 
1
BC
 
1

Length

Max length29
Median length4
Mean length4.8555133
Min length2

Characters and Unicode

Total characters1277
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st rowpass
2nd rowpass
3rd rowpass
4th rowpass
5th rowfail in ESE

Common Values

ValueCountFrequency (%)
pass 233
88.6%
fail in ESE 25
 
9.5%
absent in ESE 3
 
1.1%
Detained,att endance shortage 1
 
0.4%
BC 1
 
0.4%

Length

2024-10-04T02:15:21.890201image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:22.013811image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
pass 233
72.6%
in 28
 
8.7%
ese 28
 
8.7%
fail 25
 
7.8%
absent 3
 
0.9%
detained,att 1
 
0.3%
endance 1
 
0.3%
shortage 1
 
0.3%
bc 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
s 470
36.8%
a 265
20.8%
p 233
18.2%
E 56
 
4.4%
i 54
 
4.2%
53
 
4.2%
n 34
 
2.7%
S 28
 
2.2%
l 25
 
2.0%
f 25
 
2.0%
Other values (14) 34
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1131
88.6%
Uppercase Letter 87
 
6.8%
Space Separator 53
 
4.2%
Control 5
 
0.4%
Other Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 470
41.6%
a 265
23.4%
p 233
20.6%
i 54
 
4.8%
n 34
 
3.0%
l 25
 
2.2%
f 25
 
2.2%
e 8
 
0.7%
t 7
 
0.6%
b 3
 
0.3%
Other values (6) 7
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
E 56
64.4%
S 28
32.2%
D 1
 
1.1%
B 1
 
1.1%
C 1
 
1.1%
Space Separator
ValueCountFrequency (%)
53
100.0%
Control
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1218
95.4%
Common 59
 
4.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 470
38.6%
a 265
21.8%
p 233
19.1%
E 56
 
4.6%
i 54
 
4.4%
n 34
 
2.8%
S 28
 
2.3%
l 25
 
2.1%
f 25
 
2.1%
e 8
 
0.7%
Other values (11) 20
 
1.6%
Common
ValueCountFrequency (%)
53
89.8%
5
 
8.5%
, 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 470
36.8%
a 265
20.8%
p 233
18.2%
E 56
 
4.4%
i 54
 
4.2%
53
 
4.2%
n 34
 
2.7%
S 28
 
2.2%
l 25
 
2.0%
f 25
 
2.0%
Other values (14) 34
 
2.7%

Maths_Grade
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
AA
57 
AB
49 
BB
44 
BC
32 
FF
26 
Other values (6)
55 

Length

Max length2
Median length2
Mean length1.9923954
Min length1

Characters and Unicode

Total characters524
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st rowBB
2nd rowAA
3rd rowBB
4th rowDD
5th rowFF

Common Values

ValueCountFrequency (%)
AA 57
21.7%
AB 49
18.6%
BB 44
16.7%
BC 32
12.2%
FF 26
9.9%
CC 20
 
7.6%
CD 17
 
6.5%
DD 14
 
5.3%
NP 2
 
0.8%
X 1
 
0.4%

Length

2024-10-04T02:15:22.133923image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aa 57
21.7%
ab 49
18.6%
bb 44
16.7%
bc 32
12.2%
ff 26
9.9%
cc 20
 
7.6%
cd 17
 
6.5%
dd 14
 
5.3%
np 2
 
0.8%
x 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
B 169
32.3%
A 163
31.1%
C 89
17.0%
F 52
 
9.9%
D 45
 
8.6%
N 2
 
0.4%
P 2
 
0.4%
X 1
 
0.2%
4 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 523
99.8%
Decimal Number 1
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 169
32.3%
A 163
31.2%
C 89
17.0%
F 52
 
9.9%
D 45
 
8.6%
N 2
 
0.4%
P 2
 
0.4%
X 1
 
0.2%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 523
99.8%
Common 1
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 169
32.3%
A 163
31.2%
C 89
17.0%
F 52
 
9.9%
D 45
 
8.6%
N 2
 
0.4%
P 2
 
0.4%
X 1
 
0.2%
Common
ValueCountFrequency (%)
4 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 169
32.3%
A 163
31.1%
C 89
17.0%
F 52
 
9.9%
D 45
 
8.6%
N 2
 
0.4%
P 2
 
0.4%
X 1
 
0.2%
4 1
 
0.2%

Maths_Credit
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
4
233 
0
29 
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters263
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row0

Common Values

ValueCountFrequency (%)
4 233
88.6%
0 29
 
11.0%
7 1
 
0.4%

Length

2024-10-04T02:15:22.220876image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:22.329559image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
4 233
88.6%
0 29
 
11.0%
7 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
4 233
88.6%
0 29
 
11.0%
7 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 263
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 233
88.6%
0 29
 
11.0%
7 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 233
88.6%
0 29
 
11.0%
7 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 233
88.6%
0 29
 
11.0%
7 1
 
0.4%

Maths_GPA
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1330798
Minimum0
Maximum28
Zeros29
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:22.399578image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q39
95-th percentile10
Maximum28
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2832807
Coefficient of variation (CV)0.46028935
Kurtosis6.0684247
Mean7.1330798
Median Absolute Deviation (MAD)2
Skewness-0.040554772
Sum1876
Variance10.779932
MonotonicityNot monotonic
2024-10-04T02:15:22.473905image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 57
21.7%
9 49
18.6%
8 44
16.7%
7 32
12.2%
0 29
11.0%
6 20
 
7.6%
5 17
 
6.5%
4 14
 
5.3%
28 1
 
0.4%
ValueCountFrequency (%)
0 29
11.0%
4 14
 
5.3%
5 17
 
6.5%
6 20
 
7.6%
7 32
12.2%
8 44
16.7%
9 49
18.6%
10 57
21.7%
28 1
 
0.4%
ValueCountFrequency (%)
28 1
 
0.4%
10 57
21.7%
9 49
18.6%
8 44
16.7%
7 32
12.2%
6 20
 
7.6%
5 17
 
6.5%
4 14
 
5.3%
0 29
11.0%

Maths_GPC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.188213
Minimum0
Maximum40
Zeros29
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:22.534304image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124
median32
Q336
95-th percentile40
Maximum40
Range40
Interquartile range (IQR)12

Descriptive statistics

Standard deviation12.081298
Coefficient of variation (CV)0.42859397
Kurtosis0.63230016
Mean28.188213
Median Absolute Deviation (MAD)8
Skewness-1.2350469
Sum7413.5
Variance145.95776
MonotonicityNot monotonic
2024-10-04T02:15:22.649601image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
40 57
21.7%
36 49
18.6%
32 44
16.7%
28 32
12.2%
0 29
11.0%
24 20
 
7.6%
20 17
 
6.5%
16 14
 
5.3%
21.5 1
 
0.4%
ValueCountFrequency (%)
0 29
11.0%
16 14
 
5.3%
20 17
 
6.5%
21.5 1
 
0.4%
24 20
 
7.6%
28 32
12.2%
32 44
16.7%
36 49
18.6%
40 57
21.7%
ValueCountFrequency (%)
40 57
21.7%
36 49
18.6%
32 44
16.7%
28 32
12.2%
24 20
 
7.6%
21.5 1
 
0.4%
20 17
 
6.5%
16 14
 
5.3%
0 29
11.0%

BEE_ESE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct122
Distinct (%)47.3%
Missing5
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean47.04845
Minimum0
Maximum91
Zeros2
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:22.762939image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.85
Q132.625
median48
Q363
95-th percentile79.075
Maximum91
Range91
Interquartile range (IQR)30.375

Descriptive statistics

Standard deviation21.073214
Coefficient of variation (CV)0.44790453
Kurtosis-0.61975558
Mean47.04845
Median Absolute Deviation (MAD)15.25
Skewness-0.2439186
Sum12138.5
Variance444.08033
MonotonicityNot monotonic
2024-10-04T02:15:22.869660image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 12
 
4.6%
49 7
 
2.7%
50 6
 
2.3%
35 5
 
1.9%
24 5
 
1.9%
62 4
 
1.5%
73.5 4
 
1.5%
33 4
 
1.5%
27 4
 
1.5%
65.5 4
 
1.5%
Other values (112) 203
77.2%
(Missing) 5
 
1.9%
ValueCountFrequency (%)
0 2
0.8%
1 2
0.8%
2 3
1.1%
4 2
0.8%
5 1
 
0.4%
7.5 1
 
0.4%
8 2
0.8%
9 2
0.8%
10 1
 
0.4%
11.5 1
 
0.4%
ValueCountFrequency (%)
91 1
0.4%
86.5 1
0.4%
85.5 1
0.4%
84 2
0.8%
83 2
0.8%
82 1
0.4%
81.5 1
0.4%
81 1
0.4%
80.5 1
0.4%
79.5 2
0.8%

BEE_Total
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)26.8%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean57.168582
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:22.974994image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile27
Q149
median58
Q370
95-th percentile80
Maximum90
Range88
Interquartile range (IQR)21

Descriptive statistics

Standard deviation16.372418
Coefficient of variation (CV)0.28638839
Kurtosis0.066522916
Mean57.168582
Median Absolute Deviation (MAD)11
Skewness-0.5562158
Sum14921
Variance268.05609
MonotonicityNot monotonic
2024-10-04T02:15:23.101228image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 10
 
3.8%
56 10
 
3.8%
70 9
 
3.4%
51 8
 
3.0%
75 8
 
3.0%
63 8
 
3.0%
66 7
 
2.7%
55 7
 
2.7%
71 7
 
2.7%
57 7
 
2.7%
Other values (60) 180
68.4%
ValueCountFrequency (%)
2 1
0.4%
11 1
0.4%
15 1
0.4%
18 2
0.8%
19 1
0.4%
20 1
0.4%
22 2
0.8%
24 2
0.8%
26 2
0.8%
27 1
0.4%
ValueCountFrequency (%)
90 1
 
0.4%
88 1
 
0.4%
86 2
 
0.8%
84 1
 
0.4%
83 1
 
0.4%
82 2
 
0.8%
81 4
1.5%
80 5
1.9%
79 5
1.9%
78 3
1.1%

BEE_Status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size16.0 KiB
pass
220 
fail in ESE
34 
absent in ESE
 
6
fail
 
1
overall fail but pass in ESE
 
1

Length

Max length28
Median length4
Mean length5.1939163
Min length2

Characters and Unicode

Total characters1366
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.1%

Sample

1st rowpass
2nd rowpass
3rd rowpass
4th rowfail in ESE
5th rowfail in ESE

Common Values

ValueCountFrequency (%)
pass 220
83.7%
fail in ESE 34
 
12.9%
absent in ESE 6
 
2.3%
fail 1
 
0.4%
overall fail but pass in ESE 1
 
0.4%
FF 1
 
0.4%

Length

2024-10-04T02:15:23.203938image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:23.305224image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
pass 221
63.5%
in 41
 
11.8%
ese 41
 
11.8%
fail 36
 
10.3%
absent 6
 
1.7%
overall 1
 
0.3%
but 1
 
0.3%
ff 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
s 448
32.8%
a 264
19.3%
p 221
16.2%
E 82
 
6.0%
i 77
 
5.6%
77
 
5.6%
n 47
 
3.4%
S 41
 
3.0%
l 38
 
2.8%
f 36
 
2.6%
Other values (9) 35
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1156
84.6%
Uppercase Letter 125
 
9.2%
Space Separator 77
 
5.6%
Control 8
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 448
38.8%
a 264
22.8%
p 221
19.1%
i 77
 
6.7%
n 47
 
4.1%
l 38
 
3.3%
f 36
 
3.1%
b 7
 
0.6%
e 7
 
0.6%
t 7
 
0.6%
Other values (4) 4
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
E 82
65.6%
S 41
32.8%
F 2
 
1.6%
Space Separator
ValueCountFrequency (%)
77
100.0%
Control
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1281
93.8%
Common 85
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 448
35.0%
a 264
20.6%
p 221
17.3%
E 82
 
6.4%
i 77
 
6.0%
n 47
 
3.7%
S 41
 
3.2%
l 38
 
3.0%
f 36
 
2.8%
b 7
 
0.5%
Other values (7) 20
 
1.6%
Common
ValueCountFrequency (%)
77
90.6%
8
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 448
32.8%
a 264
19.3%
p 221
16.2%
E 82
 
6.0%
i 77
 
5.6%
77
 
5.6%
n 47
 
3.4%
S 41
 
3.0%
l 38
 
2.8%
f 36
 
2.6%
Other values (9) 35
 
2.6%

BEE_Grade
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
BC
57 
BB
47 
AB
45 
CC
42 
FF
38 
Other values (5)
34 

Length

Max length2
Median length2
Mean length1.9961977
Min length1

Characters and Unicode

Total characters525
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowBB
2nd rowAB
3rd rowBB
4th rowFF
5th rowFF

Common Values

ValueCountFrequency (%)
BC 57
21.7%
BB 47
17.9%
AB 45
17.1%
CC 42
16.0%
FF 38
14.4%
AA 17
 
6.5%
CD 10
 
3.8%
NP 4
 
1.5%
DD 2
 
0.8%
0 1
 
0.4%

Length

2024-10-04T02:15:23.434108image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:23.549761image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
bc 57
21.7%
bb 47
17.9%
ab 45
17.1%
cc 42
16.0%
ff 38
14.4%
aa 17
 
6.5%
cd 10
 
3.8%
np 4
 
1.5%
dd 2
 
0.8%
0 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
B 196
37.3%
C 151
28.8%
A 79
15.0%
F 76
 
14.5%
D 14
 
2.7%
N 4
 
0.8%
P 4
 
0.8%
0 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 524
99.8%
Decimal Number 1
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 196
37.4%
C 151
28.8%
A 79
15.1%
F 76
 
14.5%
D 14
 
2.7%
N 4
 
0.8%
P 4
 
0.8%
Decimal Number
ValueCountFrequency (%)
0 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 524
99.8%
Common 1
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 196
37.4%
C 151
28.8%
A 79
15.1%
F 76
 
14.5%
D 14
 
2.7%
N 4
 
0.8%
P 4
 
0.8%
Common
ValueCountFrequency (%)
0 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 196
37.3%
C 151
28.8%
A 79
15.0%
F 76
 
14.5%
D 14
 
2.7%
N 4
 
0.8%
P 4
 
0.8%
0 1
 
0.2%

BEE_Credit
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
4
220 
0
43 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters263
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row0
5th row0

Common Values

ValueCountFrequency (%)
4 220
83.7%
0 43
 
16.3%

Length

2024-10-04T02:15:23.689494image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:23.761244image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
4 220
83.7%
0 43
 
16.3%

Most occurring characters

ValueCountFrequency (%)
4 220
83.7%
0 43
 
16.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 263
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 220
83.7%
0 43
 
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 220
83.7%
0 43
 
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 220
83.7%
0 43
 
16.3%

BEE_GPA
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3117871
Minimum0
Maximum10
Zeros43
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:23.815072image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median7
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0657138
Coefficient of variation (CV)0.48571248
Kurtosis0.24674108
Mean6.3117871
Median Absolute Deviation (MAD)1
Skewness-1.1865432
Sum1660
Variance9.398601
MonotonicityNot monotonic
2024-10-04T02:15:23.898875image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 57
21.7%
8 47
17.9%
9 45
17.1%
0 43
16.3%
6 42
16.0%
10 17
 
6.5%
5 10
 
3.8%
4 2
 
0.8%
ValueCountFrequency (%)
0 43
16.3%
4 2
 
0.8%
5 10
 
3.8%
6 42
16.0%
7 57
21.7%
8 47
17.9%
9 45
17.1%
10 17
 
6.5%
ValueCountFrequency (%)
10 17
 
6.5%
9 45
17.1%
8 47
17.9%
7 57
21.7%
6 42
16.0%
5 10
 
3.8%
4 2
 
0.8%
0 43
16.3%

BEE_GPC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.247148
Minimum0
Maximum40
Zeros43
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:24.009731image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124
median28
Q332
95-th percentile40
Maximum40
Range40
Interquartile range (IQR)8

Descriptive statistics

Standard deviation12.262855
Coefficient of variation (CV)0.48571248
Kurtosis0.24674108
Mean25.247148
Median Absolute Deviation (MAD)4
Skewness-1.1865432
Sum6640
Variance150.37762
MonotonicityNot monotonic
2024-10-04T02:15:24.099131image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
28 57
21.7%
32 47
17.9%
36 45
17.1%
0 43
16.3%
24 42
16.0%
40 17
 
6.5%
20 10
 
3.8%
16 2
 
0.8%
ValueCountFrequency (%)
0 43
16.3%
16 2
 
0.8%
20 10
 
3.8%
24 42
16.0%
28 57
21.7%
32 47
17.9%
36 45
17.1%
40 17
 
6.5%
ValueCountFrequency (%)
40 17
 
6.5%
36 45
17.1%
32 47
17.9%
28 57
21.7%
24 42
16.0%
20 10
 
3.8%
16 2
 
0.8%
0 43
16.3%

EM_ESE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct66
Distinct (%)25.3%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean28.16092
Minimum0
Maximum89
Zeros134
Zeros (%)51.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:24.235556image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q359
95-th percentile79
Maximum89
Range89
Interquartile range (IQR)59

Descriptive statistics

Standard deviation31.594622
Coefficient of variation (CV)1.1219315
Kurtosis-1.5198006
Mean28.16092
Median Absolute Deviation (MAD)0
Skewness0.42530892
Sum7350
Variance998.22016
MonotonicityNot monotonic
2024-10-04T02:15:25.009664image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 134
51.0%
50 7
 
2.7%
59 5
 
1.9%
60 5
 
1.9%
58 5
 
1.9%
79 4
 
1.5%
61 4
 
1.5%
64 4
 
1.5%
48 4
 
1.5%
77 3
 
1.1%
Other values (56) 86
32.7%
ValueCountFrequency (%)
0 134
51.0%
2 1
 
0.4%
5 1
 
0.4%
11 1
 
0.4%
14 1
 
0.4%
18 1
 
0.4%
19 1
 
0.4%
21 1
 
0.4%
23 1
 
0.4%
29 2
 
0.8%
ValueCountFrequency (%)
89 1
0.4%
88 1
0.4%
87 2
0.8%
85 1
0.4%
84 2
0.8%
83 1
0.4%
82 1
0.4%
81.5 1
0.4%
81 1
0.4%
80 2
0.8%

EM_Total
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.619772
Minimum0
Maximum93
Zeros133
Zeros (%)50.6%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:25.130921image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q373
95-th percentile85
Maximum93
Range93
Interquartile range (IQR)73

Descriptive statistics

Standard deviation36.965425
Coefficient of variation (CV)1.0377783
Kurtosis-1.8733715
Mean35.619772
Median Absolute Deviation (MAD)0
Skewness0.14272693
Sum9368
Variance1366.4427
MonotonicityNot monotonic
2024-10-04T02:15:25.269708image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 133
50.6%
70 9
 
3.4%
71 8
 
3.0%
74 8
 
3.0%
69 6
 
2.3%
85 6
 
2.3%
81 6
 
2.3%
77 6
 
2.3%
79 5
 
1.9%
80 5
 
1.9%
Other values (33) 71
27.0%
ValueCountFrequency (%)
0 133
50.6%
21 1
 
0.4%
44 3
 
1.1%
46 1
 
0.4%
48 2
 
0.8%
50 2
 
0.8%
53 1
 
0.4%
56 2
 
0.8%
57 1
 
0.4%
58 1
 
0.4%
ValueCountFrequency (%)
93 2
 
0.8%
91 1
 
0.4%
89 1
 
0.4%
88 1
 
0.4%
87 3
1.1%
86 1
 
0.4%
85 6
2.3%
84 2
 
0.8%
83 3
1.1%
82 2
 
0.8%

EM_Status
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size13.6 KiB

EM_Grade
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size13.2 KiB

EM_Credit
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
0
146 
3
117 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters263
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 146
55.5%
3 117
44.5%

Length

2024-10-04T02:15:25.374152image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:25.439767image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 146
55.5%
3 117
44.5%

Most occurring characters

ValueCountFrequency (%)
0 146
55.5%
3 117
44.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 263
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 146
55.5%
3 117
44.5%

Most occurring scripts

ValueCountFrequency (%)
Common 263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 146
55.5%
3 117
44.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 146
55.5%
3 117
44.5%

EM_GPA
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8326996
Minimum0
Maximum10
Zeros146
Zeros (%)55.5%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:25.510743image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.3363539
Coefficient of variation (CV)1.1314098
Kurtosis-1.8539843
Mean3.8326996
Median Absolute Deviation (MAD)0
Skewness0.28611214
Sum1008
Variance18.803965
MonotonicityNot monotonic
2024-10-04T02:15:25.581439image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 146
55.5%
9 43
 
16.3%
8 39
 
14.8%
10 22
 
8.4%
7 11
 
4.2%
6 2
 
0.8%
ValueCountFrequency (%)
0 146
55.5%
6 2
 
0.8%
7 11
 
4.2%
8 39
 
14.8%
9 43
 
16.3%
10 22
 
8.4%
ValueCountFrequency (%)
10 22
 
8.4%
9 43
 
16.3%
8 39
 
14.8%
7 11
 
4.2%
6 2
 
0.8%
0 146
55.5%

EM_GPC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.612167
Minimum0
Maximum30
Zeros145
Zeros (%)55.1%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:25.639834image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325.5
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation13.039348
Coefficient of variation (CV)1.1229039
Kurtosis-1.8615555
Mean11.612167
Median Absolute Deviation (MAD)0
Skewness0.27130181
Sum3054
Variance170.02458
MonotonicityNot monotonic
2024-10-04T02:15:25.709652image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 145
55.1%
27 43
 
16.3%
24 39
 
14.8%
30 23
 
8.7%
21 11
 
4.2%
18 2
 
0.8%
ValueCountFrequency (%)
0 145
55.1%
18 2
 
0.8%
21 11
 
4.2%
24 39
 
14.8%
27 43
 
16.3%
30 23
 
8.7%
ValueCountFrequency (%)
30 23
 
8.7%
27 43
 
16.3%
24 39
 
14.8%
21 11
 
4.2%
18 2
 
0.8%
0 145
55.1%

EG_ESE
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct66
Distinct (%)25.5%
Missing4
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean26.127413
Minimum0
Maximum99
Zeros130
Zeros (%)49.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:25.810787image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q354
95-th percentile78.2
Maximum99
Range99
Interquartile range (IQR)54

Descriptive statistics

Standard deviation30.505229
Coefficient of variation (CV)1.1675564
Kurtosis-1.0500187
Mean26.127413
Median Absolute Deviation (MAD)0
Skewness0.65079122
Sum6767
Variance930.56897
MonotonicityNot monotonic
2024-10-04T02:15:25.950721image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 130
49.4%
61 6
 
2.3%
48 5
 
1.9%
47 4
 
1.5%
56 4
 
1.5%
55 4
 
1.5%
39 4
 
1.5%
69 4
 
1.5%
27 4
 
1.5%
54 4
 
1.5%
Other values (56) 90
34.2%
ValueCountFrequency (%)
0 130
49.4%
2 1
 
0.4%
8 1
 
0.4%
9 1
 
0.4%
11 1
 
0.4%
12 2
 
0.8%
13 1
 
0.4%
14 2
 
0.8%
16 1
 
0.4%
18 3
 
1.1%
ValueCountFrequency (%)
99 1
0.4%
98 1
0.4%
94 1
0.4%
92 1
0.4%
91 2
0.8%
90 1
0.4%
89 1
0.4%
88 1
0.4%
87 1
0.4%
85 1
0.4%

EG_Total
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)21.1%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean35.218391
Minimum0
Maximum96
Zeros130
Zeros (%)49.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:26.105635image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q374
95-th percentile87
Maximum96
Range96
Interquartile range (IQR)74

Descriptive statistics

Standard deviation36.685281
Coefficient of variation (CV)1.0416512
Kurtosis-1.7821949
Mean35.218391
Median Absolute Deviation (MAD)9
Skewness0.19777143
Sum9192
Variance1345.8098
MonotonicityNot monotonic
2024-10-04T02:15:26.228971image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 130
49.4%
80 8
 
3.0%
76 7
 
2.7%
77 6
 
2.3%
69 6
 
2.3%
65 4
 
1.5%
78 4
 
1.5%
82 4
 
1.5%
61 4
 
1.5%
86 4
 
1.5%
Other values (45) 84
31.9%
ValueCountFrequency (%)
0 130
49.4%
9 1
 
0.4%
33 1
 
0.4%
38 2
 
0.8%
39 1
 
0.4%
41 1
 
0.4%
43 1
 
0.4%
44 1
 
0.4%
45 1
 
0.4%
46 1
 
0.4%
ValueCountFrequency (%)
96 1
 
0.4%
95 1
 
0.4%
94 1
 
0.4%
92 2
0.8%
90 3
1.1%
89 1
 
0.4%
88 3
1.1%
87 2
0.8%
86 4
1.5%
85 1
 
0.4%

EG_Status
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size13.7 KiB

EG_Grade
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size13.2 KiB

EG_Credit
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
0
151 
3
111 
8
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters263
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 151
57.4%
3 111
42.2%
8 1
 
0.4%

Length

2024-10-04T02:15:26.368871image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:26.450900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 151
57.4%
3 111
42.2%
8 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 151
57.4%
3 111
42.2%
8 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 263
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 151
57.4%
3 111
42.2%
8 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 151
57.4%
3 111
42.2%
8 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 151
57.4%
3 111
42.2%
8 1
 
0.4%

EG_GPA
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6387833
Minimum0
Maximum24
Zeros151
Zeros (%)57.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:26.503945image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.4226535
Coefficient of variation (CV)1.215421
Kurtosis-0.18893206
Mean3.6387833
Median Absolute Deviation (MAD)0
Skewness0.69804897
Sum957
Variance19.559864
MonotonicityNot monotonic
2024-10-04T02:15:26.579687image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 151
57.4%
9 39
 
14.8%
10 23
 
8.7%
8 23
 
8.7%
7 16
 
6.1%
6 6
 
2.3%
5 4
 
1.5%
24 1
 
0.4%
ValueCountFrequency (%)
0 151
57.4%
5 4
 
1.5%
6 6
 
2.3%
7 16
 
6.1%
8 23
 
8.7%
9 39
 
14.8%
10 23
 
8.7%
24 1
 
0.4%
ValueCountFrequency (%)
24 1
 
0.4%
10 23
 
8.7%
9 39
 
14.8%
8 23
 
8.7%
7 16
 
6.1%
6 6
 
2.3%
5 4
 
1.5%
0 151
57.4%

EG_GPC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.901141
Minimum0
Maximum68
Zeros151
Zeros (%)57.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:26.639789image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324
95-th percentile30
Maximum68
Range68
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.19979
Coefficient of variation (CV)1.2108632
Kurtosis-0.55169692
Mean10.901141
Median Absolute Deviation (MAD)0
Skewness0.64232322
Sum2867
Variance174.23446
MonotonicityNot monotonic
2024-10-04T02:15:26.709522image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 151
57.4%
27 39
 
14.8%
30 23
 
8.7%
24 23
 
8.7%
21 16
 
6.1%
18 6
 
2.3%
15 4
 
1.5%
68 1
 
0.4%
ValueCountFrequency (%)
0 151
57.4%
15 4
 
1.5%
18 6
 
2.3%
21 16
 
6.1%
24 23
 
8.7%
27 39
 
14.8%
30 23
 
8.7%
68 1
 
0.4%
ValueCountFrequency (%)
68 1
 
0.4%
30 23
 
8.7%
27 39
 
14.8%
24 23
 
8.7%
21 16
 
6.1%
18 6
 
2.3%
15 4
 
1.5%
0 151
57.4%

SS_Total
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)16.8%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean76.973282
Minimum32
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:26.811105image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile59
Q173
median79
Q383
95-th percentile88.95
Maximum94
Range62
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.42663
Coefficient of variation (CV)0.12246626
Kurtosis2.705578
Mean76.973282
Median Absolute Deviation (MAD)5.5
Skewness-1.2860093
Sum20167
Variance88.861352
MonotonicityNot monotonic
2024-10-04T02:15:26.935798image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
81 20
 
7.6%
78 15
 
5.7%
82 14
 
5.3%
80 14
 
5.3%
83 13
 
4.9%
73 13
 
4.9%
74 12
 
4.6%
86 11
 
4.2%
79 11
 
4.2%
84 10
 
3.8%
Other values (34) 129
49.0%
ValueCountFrequency (%)
32 1
 
0.4%
38 1
 
0.4%
48 1
 
0.4%
49 1
 
0.4%
52 1
 
0.4%
53 2
0.8%
54 1
 
0.4%
57 2
0.8%
58 3
1.1%
59 2
0.8%
ValueCountFrequency (%)
94 1
 
0.4%
92 2
 
0.8%
91 1
 
0.4%
90 4
 
1.5%
89 6
2.3%
88 9
3.4%
87 10
3.8%
86 11
4.2%
85 10
3.8%
84 10
3.8%

SS_Status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
pass
260 
fail
 
2
BC
 
1

Length

Max length4
Median length4
Mean length3.9923954
Min length2

Characters and Unicode

Total characters1050
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowpass
2nd rowpass
3rd rowpass
4th rowpass
5th rowpass

Common Values

ValueCountFrequency (%)
pass 260
98.9%
fail 2
 
0.8%
BC 1
 
0.4%

Length

2024-10-04T02:15:27.069465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:27.162163image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
pass 260
98.9%
fail 2
 
0.8%
bc 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
s 520
49.5%
a 262
25.0%
p 260
24.8%
f 2
 
0.2%
i 2
 
0.2%
l 2
 
0.2%
B 1
 
0.1%
C 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1048
99.8%
Uppercase Letter 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 520
49.6%
a 262
25.0%
p 260
24.8%
f 2
 
0.2%
i 2
 
0.2%
l 2
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
C 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1050
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 520
49.5%
a 262
25.0%
p 260
24.8%
f 2
 
0.2%
i 2
 
0.2%
l 2
 
0.2%
B 1
 
0.1%
C 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 520
49.5%
a 262
25.0%
p 260
24.8%
f 2
 
0.2%
i 2
 
0.2%
l 2
 
0.2%
B 1
 
0.1%
C 1
 
0.1%

SS_Grade
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
AB
94 
AA
64 
BB
55 
BC
28 
CC
13 
Other values (4)
 
9

Length

Max length2
Median length2
Mean length1.9961977
Min length1

Characters and Unicode

Total characters525
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st rowAA
2nd rowAA
3rd rowBC
4th rowBB
5th rowCC

Common Values

ValueCountFrequency (%)
AB 94
35.7%
AA 64
24.3%
BB 55
20.9%
BC 28
 
10.6%
CC 13
 
4.9%
CD 5
 
1.9%
FF 2
 
0.8%
DD 1
 
0.4%
2 1
 
0.4%

Length

2024-10-04T02:15:27.262868image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:27.391145image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ab 94
35.7%
aa 64
24.3%
bb 55
20.9%
bc 28
 
10.6%
cc 13
 
4.9%
cd 5
 
1.9%
ff 2
 
0.8%
dd 1
 
0.4%
2 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
B 232
44.2%
A 222
42.3%
C 59
 
11.2%
D 7
 
1.3%
F 4
 
0.8%
2 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 524
99.8%
Decimal Number 1
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 232
44.3%
A 222
42.4%
C 59
 
11.3%
D 7
 
1.3%
F 4
 
0.8%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 524
99.8%
Common 1
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 232
44.3%
A 222
42.4%
C 59
 
11.3%
D 7
 
1.3%
F 4
 
0.8%
Common
ValueCountFrequency (%)
2 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 232
44.2%
A 222
42.3%
C 59
 
11.2%
D 7
 
1.3%
F 4
 
0.8%
2 1
 
0.2%

SS_Credit
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
2
260 
0
 
2
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters263
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 260
98.9%
0 2
 
0.8%
7 1
 
0.4%

Length

2024-10-04T02:15:27.540988image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:27.649700image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 260
98.9%
0 2
 
0.8%
7 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
2 260
98.9%
0 2
 
0.8%
7 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 263
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 260
98.9%
0 2
 
0.8%
7 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 260
98.9%
0 2
 
0.8%
7 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 260
98.9%
0 2
 
0.8%
7 1
 
0.4%

SS_GPA
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5285171
Minimum0
Maximum14
Zeros2
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:27.739498image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q18
median9
Q39
95-th percentile10
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4898334
Coefficient of variation (CV)0.17468844
Kurtosis7.7980047
Mean8.5285171
Median Absolute Deviation (MAD)1
Skewness-1.7374696
Sum2243
Variance2.2196035
MonotonicityNot monotonic
2024-10-04T02:15:27.844138image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
9 94
35.7%
10 64
24.3%
8 55
20.9%
7 28
 
10.6%
6 13
 
4.9%
5 5
 
1.9%
0 2
 
0.8%
4 1
 
0.4%
14 1
 
0.4%
ValueCountFrequency (%)
0 2
 
0.8%
4 1
 
0.4%
5 5
 
1.9%
6 13
 
4.9%
7 28
 
10.6%
8 55
20.9%
9 94
35.7%
10 64
24.3%
14 1
 
0.4%
ValueCountFrequency (%)
14 1
 
0.4%
10 64
24.3%
9 94
35.7%
8 55
20.9%
7 28
 
10.6%
6 13
 
4.9%
5 5
 
1.9%
4 1
 
0.4%
0 2
 
0.8%

SS_GPC
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.258555
Minimum0
Maximum81
Zeros2
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:27.932068image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q116
median18
Q318
95-th percentile20
Maximum81
Range81
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.897584
Coefficient of variation (CV)0.28377717
Kurtosis110.47649
Mean17.258555
Median Absolute Deviation (MAD)2
Skewness8.0008372
Sum4539
Variance23.986329
MonotonicityNot monotonic
2024-10-04T02:15:28.039585image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
18 94
35.7%
20 64
24.3%
16 55
20.9%
14 28
 
10.6%
12 13
 
4.9%
10 5
 
1.9%
0 2
 
0.8%
8 1
 
0.4%
81 1
 
0.4%
ValueCountFrequency (%)
0 2
 
0.8%
8 1
 
0.4%
10 5
 
1.9%
12 13
 
4.9%
14 28
 
10.6%
16 55
20.9%
18 94
35.7%
20 64
24.3%
81 1
 
0.4%
ValueCountFrequency (%)
81 1
 
0.4%
20 64
24.3%
18 94
35.7%
16 55
20.9%
14 28
 
10.6%
12 13
 
4.9%
10 5
 
1.9%
8 1
 
0.4%
0 2
 
0.8%

PSOOP_Total
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)19.1%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean78.278626
Minimum40
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:28.129451image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile55.05
Q174
median80
Q386
95-th percentile93
Maximum97
Range57
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.133234
Coefficient of variation (CV)0.14222572
Kurtosis0.69846126
Mean78.278626
Median Absolute Deviation (MAD)6
Skewness-0.9365275
Sum20509
Variance123.94889
MonotonicityNot monotonic
2024-10-04T02:15:28.240944image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 15
 
5.7%
85 14
 
5.3%
83 14
 
5.3%
84 13
 
4.9%
78 13
 
4.9%
81 12
 
4.6%
86 11
 
4.2%
75 10
 
3.8%
80 9
 
3.4%
87 8
 
3.0%
Other values (40) 143
54.4%
ValueCountFrequency (%)
40 1
 
0.4%
41 1
 
0.4%
46 1
 
0.4%
48 1
 
0.4%
50 1
 
0.4%
51 1
 
0.4%
52 1
 
0.4%
53 2
0.8%
54 2
0.8%
55 3
1.1%
ValueCountFrequency (%)
97 1
 
0.4%
96 3
 
1.1%
95 2
 
0.8%
94 5
1.9%
93 5
1.9%
92 6
2.3%
91 8
3.0%
90 6
2.3%
89 7
2.7%
88 7
2.7%

PSOOP_Status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
pass
262 
AB
 
1

Length

Max length4
Median length4
Mean length3.9923954
Min length2

Characters and Unicode

Total characters1050
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowpass
2nd rowpass
3rd rowpass
4th rowpass
5th rowpass

Common Values

ValueCountFrequency (%)
pass 262
99.6%
AB 1
 
0.4%

Length

2024-10-04T02:15:28.350905image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:28.443008image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
pass 262
99.6%
ab 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
s 524
49.9%
p 262
25.0%
a 262
25.0%
A 1
 
0.1%
B 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1048
99.8%
Uppercase Letter 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 524
50.0%
p 262
25.0%
a 262
25.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1050
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 524
49.9%
p 262
25.0%
a 262
25.0%
A 1
 
0.1%
B 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 524
49.9%
p 262
25.0%
a 262
25.0%
A 1
 
0.1%
B 1
 
0.1%

PSOOP_Grade
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
AB
89 
AA
69 
BB
55 
BC
26 
CC
14 
Other values (3)
10 

Length

Max length2
Median length2
Mean length1.9961977
Min length1

Characters and Unicode

Total characters525
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowAA
2nd rowAA
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
AB 89
33.8%
AA 69
26.2%
BB 55
20.9%
BC 26
 
9.9%
CC 14
 
5.3%
CD 7
 
2.7%
DD 2
 
0.8%
3 1
 
0.4%

Length

2024-10-04T02:15:28.541129image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:28.609574image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ab 89
33.8%
aa 69
26.2%
bb 55
20.9%
bc 26
 
9.9%
cc 14
 
5.3%
cd 7
 
2.7%
dd 2
 
0.8%
3 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 227
43.2%
B 225
42.9%
C 61
 
11.6%
D 11
 
2.1%
3 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 524
99.8%
Decimal Number 1
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 227
43.3%
B 225
42.9%
C 61
 
11.6%
D 11
 
2.1%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 524
99.8%
Common 1
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 227
43.3%
B 225
42.9%
C 61
 
11.6%
D 11
 
2.1%
Common
ValueCountFrequency (%)
3 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 227
43.2%
B 225
42.9%
C 61
 
11.6%
D 11
 
2.1%
3 1
 
0.2%

PSOOP_Credit
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
3
262 
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters263
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 262
99.6%
9 1
 
0.4%

Length

2024-10-04T02:15:28.698955image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:28.777359image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
3 262
99.6%
9 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
3 262
99.6%
9 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 263
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 262
99.6%
9 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 262
99.6%
9 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 262
99.6%
9 1
 
0.4%

PSOOP_GPA
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6197719
Minimum4
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:28.829569image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q18
median9
Q310
95-th percentile10
Maximum27
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7495661
Coefficient of variation (CV)0.20297128
Kurtosis45.873199
Mean8.6197719
Median Absolute Deviation (MAD)1
Skewness3.9436185
Sum2267
Variance3.0609816
MonotonicityNot monotonic
2024-10-04T02:15:28.904914image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
9 89
33.8%
10 69
26.2%
8 55
20.9%
7 26
 
9.9%
6 14
 
5.3%
5 7
 
2.7%
4 2
 
0.8%
27 1
 
0.4%
ValueCountFrequency (%)
4 2
 
0.8%
5 7
 
2.7%
6 14
 
5.3%
7 26
 
9.9%
8 55
20.9%
9 89
33.8%
10 69
26.2%
27 1
 
0.4%
ValueCountFrequency (%)
27 1
 
0.4%
10 69
26.2%
9 89
33.8%
8 55
20.9%
7 26
 
9.9%
6 14
 
5.3%
5 7
 
2.7%
4 2
 
0.8%

PSOOP_GPC
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.878327
Minimum12
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:29.014956image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile18
Q124
median27
Q330
95-th percentile30
Maximum86
Range74
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.4542184
Coefficient of variation (CV)0.21076395
Kurtosis55.775047
Mean25.878327
Median Absolute Deviation (MAD)3
Skewness4.6859271
Sum6806
Variance29.748498
MonotonicityNot monotonic
2024-10-04T02:15:29.111097image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
27 89
33.8%
30 69
26.2%
24 55
20.9%
21 26
 
9.9%
18 14
 
5.3%
15 7
 
2.7%
12 2
 
0.8%
86 1
 
0.4%
ValueCountFrequency (%)
12 2
 
0.8%
15 7
 
2.7%
18 14
 
5.3%
21 26
 
9.9%
24 55
20.9%
27 89
33.8%
30 69
26.2%
86 1
 
0.4%
ValueCountFrequency (%)
86 1
 
0.4%
30 69
26.2%
27 89
33.8%
24 55
20.9%
21 26
 
9.9%
18 14
 
5.3%
15 7
 
2.7%
12 2
 
0.8%

UHV_Total
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)14.2%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean79.716475
Minimum47
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:29.229767image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile66
Q176
median81
Q386
95-th percentile89
Maximum93
Range46
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.6874071
Coefficient of variation (CV)0.096434358
Kurtosis2.7054578
Mean79.716475
Median Absolute Deviation (MAD)5
Skewness-1.3193094
Sum20806
Variance59.096228
MonotonicityNot monotonic
2024-10-04T02:15:29.359715image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
86 38
 
14.4%
81 19
 
7.2%
76 19
 
7.2%
82 17
 
6.5%
83 16
 
6.1%
79 14
 
5.3%
78 11
 
4.2%
80 11
 
4.2%
77 11
 
4.2%
87 10
 
3.8%
Other values (27) 95
36.1%
ValueCountFrequency (%)
47 1
0.4%
51 1
0.4%
52 1
0.4%
53 1
0.4%
55 1
0.4%
56 1
0.4%
58 1
0.4%
60 1
0.4%
61 2
0.8%
63 1
0.4%
ValueCountFrequency (%)
93 3
 
1.1%
92 3
 
1.1%
90 5
 
1.9%
89 10
 
3.8%
88 3
 
1.1%
87 10
 
3.8%
86 38
14.4%
85 7
 
2.7%
84 9
 
3.4%
83 16
6.1%

UHV_Status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
pass
260 
absent
 
1
#REF!
 
1
AA
 
1

Length

Max length6
Median length4
Mean length4.0038023
Min length2

Characters and Unicode

Total characters1053
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.1%

Sample

1st rowpass
2nd rowpass
3rd rowpass
4th rowpass
5th rowpass

Common Values

ValueCountFrequency (%)
pass 260
98.9%
absent 1
 
0.4%
#REF! 1
 
0.4%
AA 1
 
0.4%

Length

2024-10-04T02:15:29.485253image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:29.579565image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
pass 260
98.9%
absent 1
 
0.4%
ref 1
 
0.4%
aa 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
s 521
49.5%
a 261
24.8%
p 260
24.7%
A 2
 
0.2%
b 1
 
0.1%
e 1
 
0.1%
n 1
 
0.1%
# 1
 
0.1%
t 1
 
0.1%
R 1
 
0.1%
Other values (3) 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1046
99.3%
Uppercase Letter 5
 
0.5%
Other Punctuation 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 521
49.8%
a 261
25.0%
p 260
24.9%
b 1
 
0.1%
e 1
 
0.1%
n 1
 
0.1%
t 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
A 2
40.0%
R 1
20.0%
E 1
20.0%
F 1
20.0%
Other Punctuation
ValueCountFrequency (%)
# 1
50.0%
! 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1051
99.8%
Common 2
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 521
49.6%
a 261
24.8%
p 260
24.7%
A 2
 
0.2%
b 1
 
0.1%
e 1
 
0.1%
n 1
 
0.1%
t 1
 
0.1%
R 1
 
0.1%
E 1
 
0.1%
Common
ValueCountFrequency (%)
# 1
50.0%
! 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 521
49.5%
a 261
24.8%
p 260
24.7%
A 2
 
0.2%
b 1
 
0.1%
e 1
 
0.1%
n 1
 
0.1%
# 1
 
0.1%
t 1
 
0.1%
R 1
 
0.1%
Other values (3) 3
 
0.3%

UHV_Grade
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
AB
104 
AA
72 
BB
64 
BC
11 
CC
 
6
Other values (3)
 
6

Length

Max length2
Median length2
Mean length1.9961977
Min length1

Characters and Unicode

Total characters525
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st rowBB
2nd rowAA
3rd rowBB
4th rowBB
5th rowBC

Common Values

ValueCountFrequency (%)
AB 104
39.5%
AA 72
27.4%
BB 64
24.3%
BC 11
 
4.2%
CC 6
 
2.3%
CD 4
 
1.5%
FF 1
 
0.4%
2 1
 
0.4%

Length

2024-10-04T02:15:29.659558image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:29.764981image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ab 104
39.5%
aa 72
27.4%
bb 64
24.3%
bc 11
 
4.2%
cc 6
 
2.3%
cd 4
 
1.5%
ff 1
 
0.4%
2 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 248
47.2%
B 243
46.3%
C 27
 
5.1%
D 4
 
0.8%
F 2
 
0.4%
2 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 524
99.8%
Decimal Number 1
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 248
47.3%
B 243
46.4%
C 27
 
5.2%
D 4
 
0.8%
F 2
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 524
99.8%
Common 1
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 248
47.3%
B 243
46.4%
C 27
 
5.2%
D 4
 
0.8%
F 2
 
0.4%
Common
ValueCountFrequency (%)
2 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 248
47.2%
B 243
46.3%
C 27
 
5.1%
D 4
 
0.8%
F 2
 
0.4%
2 1
 
0.2%

UHV_Credit
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
2
261 
0
 
1
10
 
1

Length

Max length2
Median length1
Mean length1.0038023
Min length1

Characters and Unicode

Total characters264
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 261
99.2%
0 1
 
0.4%
10 1
 
0.4%

Length

2024-10-04T02:15:29.900939image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:29.989701image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 261
99.2%
0 1
 
0.4%
10 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
2 261
98.9%
0 2
 
0.8%
1 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 261
98.9%
0 2
 
0.8%
1 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 261
98.9%
0 2
 
0.8%
1 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 261
98.9%
0 2
 
0.8%
1 1
 
0.4%

UHV_GPA
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8250951
Minimum0
Maximum20
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:30.079643image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q18
median9
Q310
95-th percentile10
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3702877
Coefficient of variation (CV)0.15527172
Kurtosis22.649003
Mean8.8250951
Median Absolute Deviation (MAD)1
Skewness0.54341848
Sum2321
Variance1.8776884
MonotonicityNot monotonic
2024-10-04T02:15:30.155441image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
9 104
39.5%
10 72
27.4%
8 64
24.3%
7 11
 
4.2%
6 6
 
2.3%
5 4
 
1.5%
0 1
 
0.4%
20 1
 
0.4%
ValueCountFrequency (%)
0 1
 
0.4%
5 4
 
1.5%
6 6
 
2.3%
7 11
 
4.2%
8 64
24.3%
9 104
39.5%
10 72
27.4%
20 1
 
0.4%
ValueCountFrequency (%)
20 1
 
0.4%
10 72
27.4%
9 104
39.5%
8 64
24.3%
7 11
 
4.2%
6 6
 
2.3%
5 4
 
1.5%
0 1
 
0.4%

UHV_GPC
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.634981
Minimum0
Maximum36
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:30.229490image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q116
median18
Q320
95-th percentile20
Maximum36
Range36
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6247197
Coefficient of variation (CV)0.14883598
Kurtosis16.280902
Mean17.634981
Median Absolute Deviation (MAD)2
Skewness-0.42426552
Sum4638
Variance6.8891533
MonotonicityNot monotonic
2024-10-04T02:15:30.339777image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
18 104
39.5%
20 72
27.4%
16 64
24.3%
14 11
 
4.2%
12 6
 
2.3%
10 4
 
1.5%
0 1
 
0.4%
36 1
 
0.4%
ValueCountFrequency (%)
0 1
 
0.4%
10 4
 
1.5%
12 6
 
2.3%
14 11
 
4.2%
16 64
24.3%
18 104
39.5%
20 72
27.4%
36 1
 
0.4%
ValueCountFrequency (%)
36 1
 
0.4%
20 72
27.4%
18 104
39.5%
16 64
24.3%
14 11
 
4.2%
12 6
 
2.3%
10 4
 
1.5%
0 1
 
0.4%

LLC_Total
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct57
Distinct (%)22.5%
Missing10
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean81.322134
Minimum16
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:30.433922image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile52.3
Q175
median86
Q391
95-th percentile96
Maximum100
Range84
Interquartile range (IQR)16

Descriptive statistics

Standard deviation14.148097
Coefficient of variation (CV)0.17397596
Kurtosis2.077975
Mean81.322134
Median Absolute Deviation (MAD)9
Skewness-1.3018952
Sum20574.5
Variance200.16864
MonotonicityNot monotonic
2024-10-04T02:15:30.529574image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 31
 
11.8%
95 30
 
11.4%
88 16
 
6.1%
86 11
 
4.2%
75 10
 
3.8%
96 9
 
3.4%
80 9
 
3.4%
82 7
 
2.7%
65 7
 
2.7%
91 7
 
2.7%
Other values (47) 116
44.1%
(Missing) 10
 
3.8%
ValueCountFrequency (%)
16 1
 
0.4%
25 1
 
0.4%
44 1
 
0.4%
45 1
 
0.4%
46 1
 
0.4%
48 2
0.8%
50 3
1.1%
51 2
0.8%
52 1
 
0.4%
52.5 1
 
0.4%
ValueCountFrequency (%)
100 5
 
1.9%
98 1
 
0.4%
97 6
 
2.3%
96 9
 
3.4%
95 30
11.4%
94 3
 
1.1%
92 4
 
1.5%
91 7
 
2.7%
90 31
11.8%
89 4
 
1.5%

LLC_Grade
Categorical

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)3.9%
Missing6
Missing (%)2.3%
Memory size15.2 KiB
AA
132 
AB
69 
BB
29 
CC
 
8
BC
 
5
Other values (5)
14 

Length

Max length2
Median length2
Mean length1.9961089
Min length1

Characters and Unicode

Total characters513
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowBB
2nd rowAB
3rd rowBB
4th rowCC
5th rowAB

Common Values

ValueCountFrequency (%)
AA 132
50.2%
AB 69
26.2%
BB 29
 
11.0%
CC 8
 
3.0%
BC 5
 
1.9%
CD 5
 
1.9%
NG 4
 
1.5%
DD 2
 
0.8%
FF 2
 
0.8%
0 1
 
0.4%
(Missing) 6
 
2.3%

Length

2024-10-04T02:15:30.649694image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:30.760882image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
aa 132
51.4%
ab 69
26.8%
bb 29
 
11.3%
cc 8
 
3.1%
bc 5
 
1.9%
cd 5
 
1.9%
ng 4
 
1.6%
dd 2
 
0.8%
ff 2
 
0.8%
0 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 333
64.9%
B 132
 
25.7%
C 26
 
5.1%
D 9
 
1.8%
N 4
 
0.8%
G 4
 
0.8%
F 4
 
0.8%
0 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 512
99.8%
Decimal Number 1
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 333
65.0%
B 132
 
25.8%
C 26
 
5.1%
D 9
 
1.8%
N 4
 
0.8%
G 4
 
0.8%
F 4
 
0.8%
Decimal Number
ValueCountFrequency (%)
0 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 512
99.8%
Common 1
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 333
65.0%
B 132
 
25.8%
C 26
 
5.1%
D 9
 
1.8%
N 4
 
0.8%
G 4
 
0.8%
F 4
 
0.8%
Common
ValueCountFrequency (%)
0 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 333
64.9%
B 132
 
25.7%
C 26
 
5.1%
D 9
 
1.8%
N 4
 
0.8%
G 4
 
0.8%
F 4
 
0.8%
0 1
 
0.2%

LLC_Credit
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
1
250 
0
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters263
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 250
95.1%
0 13
 
4.9%

Length

2024-10-04T02:15:30.849715image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:30.929739image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 250
95.1%
0 13
 
4.9%

Most occurring characters

ValueCountFrequency (%)
1 250
95.1%
0 13
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 263
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 250
95.1%
0 13
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 250
95.1%
0 13
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 250
95.1%
0 13
 
4.9%

LLC_GPA
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7034221
Minimum0
Maximum10
Zeros13
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:31.010744image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.3222019
Coefficient of variation (CV)0.26681481
Kurtosis7.3544889
Mean8.7034221
Median Absolute Deviation (MAD)0
Skewness-2.7444812
Sum2289
Variance5.3926218
MonotonicityNot monotonic
2024-10-04T02:15:31.111175image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
10 132
50.2%
9 69
26.2%
8 29
 
11.0%
0 13
 
4.9%
6 8
 
3.0%
7 5
 
1.9%
5 5
 
1.9%
4 2
 
0.8%
ValueCountFrequency (%)
0 13
 
4.9%
4 2
 
0.8%
5 5
 
1.9%
6 8
 
3.0%
7 5
 
1.9%
8 29
 
11.0%
9 69
26.2%
10 132
50.2%
ValueCountFrequency (%)
10 132
50.2%
9 69
26.2%
8 29
 
11.0%
7 5
 
1.9%
6 8
 
3.0%
5 5
 
1.9%
4 2
 
0.8%
0 13
 
4.9%

LLC_GPC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7034221
Minimum0
Maximum10
Zeros13
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:31.170300image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.3222019
Coefficient of variation (CV)0.26681481
Kurtosis7.3544889
Mean8.7034221
Median Absolute Deviation (MAD)0
Skewness-2.7444812
Sum2289
Variance5.3926218
MonotonicityNot monotonic
2024-10-04T02:15:31.259702image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
10 132
50.2%
9 69
26.2%
8 29
 
11.0%
0 13
 
4.9%
6 8
 
3.0%
7 5
 
1.9%
5 5
 
1.9%
4 2
 
0.8%
ValueCountFrequency (%)
0 13
 
4.9%
4 2
 
0.8%
5 5
 
1.9%
6 8
 
3.0%
7 5
 
1.9%
8 29
 
11.0%
9 69
26.2%
10 132
50.2%
ValueCountFrequency (%)
10 132
50.2%
9 69
26.2%
8 29
 
11.0%
7 5
 
1.9%
6 8
 
3.0%
5 5
 
1.9%
4 2
 
0.8%
0 13
 
4.9%

Total_Credits
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
19.0
263 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1052
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19.0
2nd row19.0
3rd row19.0
4th row19.0
5th row19.0

Common Values

ValueCountFrequency (%)
19.0 263
100.0%

Length

2024-10-04T02:15:31.379499image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:31.462826image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
19.0 263
100.0%

Most occurring characters

ValueCountFrequency (%)
1 263
25.0%
9 263
25.0%
. 263
25.0%
0 263
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 789
75.0%
Other Punctuation 263
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 263
33.3%
9 263
33.3%
0 263
33.3%
Other Punctuation
ValueCountFrequency (%)
. 263
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1052
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 263
25.0%
9 263
25.0%
. 263
25.0%
0 263
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 263
25.0%
9 263
25.0%
. 263
25.0%
0 263
25.0%

Total_Credits_Earned
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.444867
Minimum6
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:31.518759image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q119
median19
Q319
95-th percentile19
Maximum19
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.3021798
Coefficient of variation (CV)0.18929235
Kurtosis3.1070167
Mean17.444867
Median Absolute Deviation (MAD)0
Skewness-2.0878191
Sum4588
Variance10.904391
MonotonicityNot monotonic
2024-10-04T02:15:31.624221image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
19 202
76.8%
15 19
 
7.2%
8 14
 
5.3%
12 9
 
3.4%
18 5
 
1.9%
7 3
 
1.1%
16 3
 
1.1%
10 3
 
1.1%
11 2
 
0.8%
14 2
 
0.8%
ValueCountFrequency (%)
6 1
 
0.4%
7 3
 
1.1%
8 14
5.3%
10 3
 
1.1%
11 2
 
0.8%
12 9
3.4%
14 2
 
0.8%
15 19
7.2%
16 3
 
1.1%
18 5
 
1.9%
ValueCountFrequency (%)
19 202
76.8%
18 5
 
1.9%
16 3
 
1.1%
15 19
 
7.2%
14 2
 
0.8%
12 9
 
3.4%
11 2
 
0.8%
10 3
 
1.1%
8 14
 
5.3%
7 3
 
1.1%

Total_GPC_Earned
Real number (ℝ)

HIGH CORRELATION 

Distinct109
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.62738
Minimum35
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:31.741044image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile59.2
Q1131
median156
Q3171
95-th percentile184.9
Maximum190
Range155
Interquartile range (IQR)40

Descriptive statistics

Standard deviation37.232275
Coefficient of variation (CV)0.25743587
Kurtosis0.66258619
Mean144.62738
Median Absolute Deviation (MAD)17
Skewness-1.2349617
Sum38037
Variance1386.2423
MonotonicityNot monotonic
2024-10-04T02:15:31.829509image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171 9
 
3.4%
159 9
 
3.4%
173 7
 
2.7%
148 6
 
2.3%
156 6
 
2.3%
186 6
 
2.3%
155 6
 
2.3%
163 6
 
2.3%
145 5
 
1.9%
154 5
 
1.9%
Other values (99) 198
75.3%
ValueCountFrequency (%)
35 1
0.4%
38 1
0.4%
39 1
0.4%
43 1
0.4%
47 1
0.4%
49 1
0.4%
50 1
0.4%
52 1
0.4%
55 1
0.4%
56 1
0.4%
ValueCountFrequency (%)
190 1
 
0.4%
189 1
 
0.4%
188 2
 
0.8%
187 2
 
0.8%
186 6
2.3%
185 2
 
0.8%
184 2
 
0.8%
183 4
1.5%
182 5
1.9%
181 4
1.5%

SGPA
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6379588
Minimum0.18421053
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-10-04T02:15:31.956746image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.18421053
5-th percentile0.31157895
Q16.605
median8.16
Q39
95-th percentile9.734
Maximum10
Range9.8157895
Interquartile range (IQR)2.395

Descriptive statistics

Standard deviation3.4836655
Coefficient of variation (CV)0.52480976
Kurtosis-0.53373266
Mean6.6379588
Median Absolute Deviation (MAD)0.95
Skewness-1.1162199
Sum1745.7832
Variance12.135925
MonotonicityNot monotonic
2024-10-04T02:15:32.080858image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 9
 
3.4%
8.37 9
 
3.4%
9.11 7
 
2.7%
9.79 6
 
2.3%
7.79 6
 
2.3%
8.21 6
 
2.3%
8.58 6
 
2.3%
8.16 6
 
2.3%
9.42 5
 
1.9%
9.58 5
 
1.9%
Other values (104) 198
75.3%
ValueCountFrequency (%)
0.1842105263 1
0.4%
0.2 1
0.4%
0.2052631579 1
0.4%
0.2263157895 1
0.4%
0.2473684211 1
0.4%
0.2578947368 1
0.4%
0.2631578947 1
0.4%
0.2736842105 1
0.4%
0.2894736842 1
0.4%
0.2947368421 1
0.4%
ValueCountFrequency (%)
10 1
 
0.4%
9.95 1
 
0.4%
9.89 2
 
0.8%
9.84 2
 
0.8%
9.79 6
2.3%
9.74 2
 
0.8%
9.68 2
 
0.8%
9.63 4
1.5%
9.58 5
1.9%
9.53 4
1.5%

Result
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size17.3 KiB
Successful
202 
Unsuccessful
61 

Length

Max length12
Median length10
Mean length10.463878
Min length10

Characters and Unicode

Total characters2752
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSuccessful
2nd rowSuccessful
3rd rowSuccessful
4th rowUnsuccessful
5th rowUnsuccessful

Common Values

ValueCountFrequency (%)
Successful 202
76.8%
Unsuccessful 61
 
23.2%

Length

2024-10-04T02:15:32.179611image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T02:15:32.273483image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
successful 202
76.8%
unsuccessful 61
 
23.2%

Most occurring characters

ValueCountFrequency (%)
s 587
21.3%
c 526
19.1%
u 526
19.1%
f 263
9.6%
e 263
9.6%
l 263
9.6%
S 202
 
7.3%
U 61
 
2.2%
n 61
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2489
90.4%
Uppercase Letter 263
 
9.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 587
23.6%
c 526
21.1%
u 526
21.1%
f 263
10.6%
e 263
10.6%
l 263
10.6%
n 61
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
S 202
76.8%
U 61
 
23.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 2752
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 587
21.3%
c 526
19.1%
u 526
19.1%
f 263
9.6%
e 263
9.6%
l 263
9.6%
S 202
 
7.3%
U 61
 
2.2%
n 61
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 587
21.3%
c 526
19.1%
u 526
19.1%
f 263
9.6%
e 263
9.6%
l 263
9.6%
S 202
 
7.3%
U 61
 
2.2%
n 61
 
2.2%

Interactions

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2024-10-04T02:14:40.662865image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:45.021011image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:48.177411image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:51.558694image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:54.876543image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:58.461545image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:02.152033image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:05.561380image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:08.310889image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:11.009674image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:13.869083image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:16.700669image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:19.450638image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:13:43.834225image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:13:47.183809image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:13:50.267568image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:13:53.316641image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:13:56.614801image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:13:59.615521image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:02.966747image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:05.515387image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:08.526687image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:11.059439image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:13.619273image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:16.233804image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:19.150212image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:21.765357image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:24.874537image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:28.552487image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:32.476440image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:35.493134image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:38.224180image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:40.776150image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:45.143899image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:48.306273image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:51.672084image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:55.000901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:58.588205image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:02.279717image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:05.664834image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:08.400984image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:11.119331image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:13.934790image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:16.783267image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:19.533623image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:13:43.950400image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:13:47.300014image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:13:50.385928image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:13:53.418979image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:13:56.738039image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:13:59.733625image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:03.033288image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:05.614653image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:08.617125image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:11.134555image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:13.666712image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:16.315704image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:19.247472image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:21.876148image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:24.988074image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:28.615550image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:32.614513image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:35.561144image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:38.288097image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:40.911166image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:45.261960image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:48.427463image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:51.783829image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:55.128549image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:14:58.710917image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:02.371701image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:05.759747image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:08.503485image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:11.253711image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:14.043869image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-04T02:15:16.879654image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-10-04T02:15:32.381240image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
BEE_CreditBEE_ESEBEE_GPABEE_GPCBEE_GradeBEE_StatusBEE_TotalEG_CreditEG_ESEEG_GPAEG_GPCEG_TotalEM_CreditEM_ESEEM_GPAEM_GPCEM_TotalLLC_CreditLLC_GPALLC_GPCLLC_GradeLLC_TotalMaths_CreditMaths_ESEMaths_GPAMaths_GPCMaths_GradeMaths_StatusMaths_TotalPSOOP_CreditPSOOP_GPAPSOOP_GPCPSOOP_GradePSOOP_StatusPSOOP_TotalResultSGPASS_CreditSS_GPASS_GPCSS_GradeSS_StatusSS_TotalTotal_Credits_EarnedTotal_GPC_EarnedUHV_CreditUHV_GPAUHV_GPCUHV_GradeUHV_StatusUHV_TotalUID
BEE_Credit1.0000.8680.9880.9880.9850.9920.8140.1480.4920.2570.2540.5010.2760.2560.2650.2430.3800.1480.1610.1610.1200.1850.5490.6630.6520.6670.6800.5510.6770.0000.4900.4900.5110.0000.5200.7910.7940.1360.5180.4090.5260.1360.5280.8890.8210.1780.4600.4810.5110.1690.5060.000
BEE_ESE0.8681.0000.9120.9120.5230.4520.9190.087-0.0450.0540.054-0.0650.3610.4200.4630.4610.4200.0210.1050.1050.0620.0990.4610.7780.7870.7920.3740.3730.7970.0000.6410.6410.2590.0000.6650.7540.8700.0000.4640.4640.2170.0000.4840.6310.8820.1340.4060.4060.2070.1140.4340.050
BEE_GPA0.9880.9121.0001.0000.9960.4200.9810.0860.0090.1140.1140.0040.3360.3770.4250.4100.3820.1420.1870.1870.0870.1770.3840.7560.7630.7820.3410.2550.7810.0000.6580.6580.2930.0000.7070.8130.9140.3350.5160.5160.2800.3350.5500.6630.9270.0000.4460.4460.2370.1370.4800.017
BEE_GPC0.9880.9121.0001.0000.9960.4200.9810.0860.0090.1140.1140.0040.3360.3770.4250.4100.3820.1420.1870.1870.0870.1770.3840.7560.7630.7820.3410.2550.7810.0000.6580.6580.2930.0000.7070.8130.9140.3350.5160.5160.2800.3350.5500.6630.9270.0000.4460.4460.2370.1370.4800.017
BEE_Grade0.9850.5230.9960.9961.0000.7080.6570.7030.1980.5380.5320.2170.3260.2440.3340.3430.3310.2840.1290.1290.3350.1110.8020.3060.6630.3540.5280.6640.3450.9850.6700.6700.4900.9850.3090.8090.5770.7740.4690.6650.4410.7740.2880.3870.4460.6930.5490.5200.4460.5850.2660.000
BEE_Status0.9920.4520.4200.4200.7081.0000.5210.7040.2210.5110.5070.2640.2830.0000.0700.1110.1510.2820.0910.0910.4320.0970.8100.3160.6020.3280.6130.6830.3820.9920.6410.6410.5560.9920.3860.7940.3350.7000.5250.6230.5290.7000.2970.4260.4160.7050.5490.5050.5300.5720.3730.000
BEE_Total0.8140.9190.9810.9810.6570.5211.0000.2070.0320.1410.1410.0190.3460.3690.4260.4260.3730.2890.1880.1880.1310.1870.6390.7890.8090.8090.4730.6750.8101.0000.6960.6960.4561.0000.7210.7130.9200.2900.5370.5370.2830.2900.5560.6360.9360.4040.4780.4780.3240.2900.4980.026
EG_Credit0.1480.0870.0860.0860.7030.7040.2071.0000.6860.9960.9960.9130.7670.5600.5290.5460.5820.2570.1340.1340.6910.0000.7130.0000.7100.1260.6980.7090.1080.9980.7020.7020.7050.9980.0000.1220.0230.7060.6980.7060.6950.7060.0830.2290.1990.7050.7050.7040.7000.7050.0000.000
EG_ESE0.492-0.0450.0090.0090.1980.2210.0320.6861.0000.9450.9450.9790.865-0.852-0.794-0.777-0.8570.0000.0330.0330.0000.0660.3990.006-0.003-0.0170.2090.320-0.0030.0950.0820.0820.2180.0950.0880.5190.0330.000-0.010-0.0100.0680.000-0.0360.0210.0500.0000.1400.1400.1030.1310.0730.029
EG_GPA0.2570.0540.1140.1140.5380.5110.1410.9960.9451.0001.0000.9480.762-0.749-0.699-0.676-0.7540.2530.0570.0570.4820.1080.7120.1000.1110.0920.5140.4990.1080.9940.1720.1720.5300.9940.1790.2400.1460.7010.0680.0680.5040.7010.0410.1770.1640.7000.2180.2180.4990.5780.1500.069
EG_GPC0.2540.0540.1140.1140.5320.5070.1410.9960.9451.0001.0000.9480.762-0.749-0.699-0.676-0.7540.2570.0570.0570.4840.1080.7170.1000.1110.0920.5270.5030.1080.9940.1720.1720.5260.9940.1790.2850.1460.7010.0680.0680.5280.7010.0410.1770.1640.7000.2180.2180.5020.5960.1500.069
EG_Total0.501-0.0650.0040.0040.2170.2640.0190.9130.9790.9480.9481.0000.885-0.852-0.795-0.795-0.8570.1970.0360.0360.1480.0730.513-0.010-0.027-0.0270.2270.292-0.0171.0000.0680.0680.3191.0000.0860.5140.0230.000-0.013-0.0130.1770.000-0.0270.0080.0420.0000.1320.1320.1790.2120.0740.043
EM_Credit0.2760.3610.3360.3360.3260.2830.3460.7670.8650.7620.7620.8851.0000.9730.9920.9850.9510.0980.0000.0000.0000.0000.3100.3280.3630.3700.3570.2970.3650.0000.2640.2640.2420.0000.2170.3700.3810.0400.1940.2180.1940.0400.1990.3570.3520.0000.1290.1640.1600.0000.1240.000
EM_ESE0.2560.4200.3770.3770.2440.0000.3690.560-0.852-0.749-0.749-0.8520.9731.0000.9710.9570.9900.0000.0160.0160.072-0.0280.1310.3590.3790.3900.1370.0000.3820.0000.2410.2410.0000.0000.2470.4050.3970.3030.2480.2480.1670.3030.2750.3120.3850.0000.0650.0650.0830.0000.124-0.007
EM_GPA0.2650.4630.4250.4250.3340.0700.4260.529-0.794-0.699-0.699-0.7950.9920.9711.0000.9870.9700.0660.0470.0470.0000.0070.1810.4090.4300.4400.2400.0830.4310.0000.2950.2950.1420.0000.2990.3650.4610.0000.2810.2810.0810.0000.3030.3870.4490.0000.0960.0960.0000.0000.1600.015
EM_GPC0.2430.4610.4100.4100.3430.1110.4260.546-0.777-0.676-0.676-0.7950.9850.9570.9871.0000.9570.0000.0300.0300.0000.0070.2260.4050.4450.4290.2530.1270.4310.1440.3120.3120.1650.1440.2990.3470.4490.0630.2970.2970.1180.0630.3030.3680.4370.0490.1140.1140.0740.0000.1600.032
EM_Total0.3800.4200.3820.3820.3310.1510.3730.582-0.857-0.754-0.754-0.8570.9510.9900.9700.9571.0000.0000.0260.0260.000-0.0200.2190.3590.3740.3850.2380.1900.3770.0000.2490.2490.1620.0000.2570.3950.3990.2920.2530.2530.2690.2920.2810.3010.3870.6920.0720.0720.3840.5590.1380.001
LLC_Credit0.1480.0210.1420.1420.2840.2820.2890.2570.0000.2530.2570.1970.0980.0000.0660.0000.0001.0000.9880.9880.9840.8130.2970.1720.2900.0410.3630.3830.1610.1130.2870.2870.3640.1130.2200.3900.3920.2580.2800.2950.2820.2580.0960.4420.3180.2570.2910.2910.3130.2500.1780.000
LLC_GPA0.1610.1050.1870.1870.1290.0910.1880.1340.0330.0570.0570.0360.0000.0160.0470.0300.0260.9881.0001.0000.9960.9160.1970.1000.0930.1120.0850.1640.1160.2160.1420.1420.1900.2160.1620.4150.2540.4570.1830.1830.2800.4570.1800.2070.2290.1090.1770.1770.2640.0970.1910.010
LLC_GPC0.1610.1050.1870.1870.1290.0910.1880.1340.0330.0570.0570.0360.0000.0160.0470.0300.0260.9881.0001.0000.9960.9160.1970.1000.0930.1120.0850.1640.1160.2160.1420.1420.1900.2160.1620.4150.2540.4570.1830.1830.2800.4570.1800.2070.2290.1090.1770.1770.2640.0970.1910.010
LLC_Grade0.1200.0620.0870.0870.3350.4320.1310.6910.0000.4820.4840.1480.0000.0720.0000.0000.0000.9840.9960.9961.0000.7150.7000.0790.4910.0000.3170.5680.0910.9840.5790.5790.4020.9840.1030.3100.1230.8170.4520.6360.4260.8170.2530.2060.1710.6860.5860.5120.4510.5600.3130.000
LLC_Total0.1850.0990.1770.1770.1110.0970.1870.0000.0660.1080.1080.0730.000-0.0280.0070.007-0.0200.8130.9160.9160.7151.0000.1720.0850.0970.0970.0000.1100.1111.0000.1190.1190.1961.0000.1320.2160.1870.3220.1450.1450.1990.3220.1350.1230.1890.0000.1770.1770.2860.0000.158-0.055
Maths_Credit0.5490.4610.3840.3840.8020.8100.6390.7130.3990.7120.7170.5130.3100.1310.1810.2260.2190.2970.1970.1970.7000.1721.0000.6920.9960.7060.9840.9960.9380.9980.7880.7880.8070.9980.5880.6480.4420.7260.7580.7550.7570.7260.3910.6280.5950.7050.7610.7460.7560.7130.4360.000
Maths_ESE0.6630.7780.7560.7560.3060.3160.7890.0000.0060.1000.100-0.0100.3280.3590.4090.4050.3590.1720.1000.1000.0790.0850.6921.0000.9430.9500.6090.5880.9620.0000.6340.6340.3070.0000.6680.7030.8430.1900.4330.4330.2420.1900.4470.5870.8650.0850.4020.4020.1900.1600.4400.020
Maths_GPA0.6520.7870.7630.7630.6630.6020.8090.710-0.0030.1110.111-0.0270.3630.3790.4300.4450.3740.2900.0930.0930.4910.0970.9960.9431.0000.9860.9880.7020.9870.9940.6370.6370.6100.9940.6570.7150.8500.7210.4630.4630.5760.7210.4580.5780.8770.7090.4070.4070.5580.5850.4290.048
Maths_GPC0.6670.7920.7820.7820.3540.3280.8090.126-0.0170.0920.092-0.0270.3700.3900.4400.4290.3850.0410.1120.1120.0000.0970.7060.9500.9861.0000.9940.4880.9870.1320.6220.6220.2720.1320.6570.7200.8650.1620.4470.4470.2310.1620.4580.6000.8920.1720.3910.3910.2060.1450.4290.031
Maths_Grade0.6800.3740.3410.3410.5280.6130.4730.6980.2090.5140.5270.2270.3570.1370.2400.2530.2380.3630.0850.0850.3170.0000.9840.6090.9880.9941.0000.9410.7610.9830.6770.6770.5240.9830.3550.7240.5280.7080.4240.6340.4060.7080.2130.4470.4410.7070.5670.4900.4420.5770.2140.000
Maths_Status0.5510.3730.2550.2550.6640.6830.6750.7090.3200.4990.5030.2920.2970.0000.0830.1270.1900.3830.1640.1640.5680.1100.9960.5880.7020.4880.9411.0000.5530.9940.6480.6480.6900.9940.5830.6420.2980.7250.6010.6290.6020.7250.3150.5150.4390.6990.5560.5350.5660.5780.3500.000
Maths_Total0.6770.7970.7810.7810.3450.3820.8100.108-0.0030.1080.108-0.0170.3650.3820.4310.4310.3770.1610.1160.1160.0910.1110.9380.9620.9870.9870.7610.5531.0001.0000.6400.6400.3381.0000.6700.7160.8620.2900.4490.4490.2440.2900.4490.5950.8880.1980.3950.3950.2670.2520.4240.029
PSOOP_Credit0.0000.0000.0000.0000.9850.9921.0000.9980.0950.9940.9941.0000.0000.0000.0000.1440.0000.1130.2160.2160.9841.0000.9980.0000.9940.1320.9830.9941.0001.0000.9960.9960.9880.4951.0000.0000.0000.9980.9880.9960.9860.9981.0000.1450.1830.9980.9940.9920.9880.9961.0000.000
PSOOP_GPA0.4900.6410.6580.6580.6700.6410.6960.7020.0820.1720.1720.0680.2640.2410.2950.3120.2490.2870.1420.1420.5790.1190.7880.6340.6370.6220.6770.6480.6400.9961.0001.0000.9920.9960.9660.5450.7570.7090.5240.5240.6530.7090.5360.5350.7680.7050.5270.5270.6420.5760.5380.051
PSOOP_GPC0.4900.6410.6580.6580.6700.6410.6960.7020.0820.1720.1720.0680.2640.2410.2950.3120.2490.2870.1420.1420.5790.1190.7880.6340.6370.6220.6770.6480.6400.9961.0001.0000.9920.9960.9660.5450.7570.7090.5240.5240.6530.7090.5360.5350.7680.7050.5270.5270.6420.5760.5380.051
PSOOP_Grade0.5110.2590.2930.2930.4900.5560.4560.7050.2180.5300.5260.3190.2420.0000.1420.1650.1620.3640.1900.1900.4020.1960.8070.3070.6100.2720.5240.6900.3380.9880.9920.9921.0000.9880.9010.6000.3910.7080.5020.6640.5100.7080.3310.3290.3940.7030.5650.5100.4500.5780.2780.000
PSOOP_Status0.0000.0000.0000.0000.9850.9921.0000.9980.0950.9940.9941.0000.0000.0000.0000.1440.0000.1130.2160.2160.9841.0000.9980.0000.9940.1320.9830.9941.0000.4950.9960.9960.9881.0001.0000.0000.0000.9980.9880.9960.9860.9981.0000.1450.1830.9980.9940.9920.9880.9961.0000.000
PSOOP_Total0.5200.6650.7070.7070.3090.3860.7210.0000.0880.1790.1790.0860.2170.2470.2990.2990.2570.2200.1620.1620.1030.1320.5880.6680.6570.6570.3550.5830.6701.0000.9660.9660.9011.0001.0000.6130.7910.1260.5260.5260.3290.1260.5530.5520.8060.0730.5440.5440.2720.0740.5660.036
Result0.7910.7540.8130.8130.8090.7940.7130.1220.5190.2400.2850.5140.3700.4050.3650.3470.3950.3900.4150.4150.3100.2160.6480.7030.7150.7200.7240.6420.7160.0000.5450.5450.6000.0000.6131.0000.9920.1750.4910.4360.5010.1750.5070.9360.8710.1330.4450.4420.4860.1640.5100.000
SGPA0.7940.8700.9140.9140.5770.3350.9200.0230.0330.1460.1460.0230.3810.3970.4610.4490.3990.3920.2540.2540.1230.1870.4420.8430.8500.8650.5280.2980.8620.0000.7570.7570.3910.0000.7910.9921.0000.0050.6110.6110.2910.0050.6250.7380.9910.0000.5460.5460.2810.0000.5810.057
SS_Credit0.1360.0000.3350.3350.7740.7000.2900.7060.0000.7010.7010.0000.0400.3030.0000.0630.2920.2580.4570.4570.8170.3220.7260.1900.7210.1620.7080.7250.2900.9980.7090.7090.7080.9980.1260.1750.0051.0000.9900.9090.9881.0000.9860.3310.2390.7040.7660.7480.7610.7020.5800.000
SS_GPA0.5180.4640.5160.5160.4690.5250.5370.698-0.0100.0680.068-0.0130.1940.2480.2810.2970.2530.2800.1830.1830.4520.1450.7580.4330.4630.4470.4240.6010.4490.9880.5240.5240.5020.9880.5260.4910.6110.9901.0001.0000.9980.9900.9650.4440.6150.6980.6290.6290.4840.5690.6560.055
SS_GPC0.4090.4640.5160.5160.6650.6230.5370.706-0.0100.0680.068-0.0130.2180.2480.2810.2970.2530.2950.1830.1830.6360.1450.7550.4330.4630.4470.6340.6290.4490.9960.5240.5240.6640.9960.5260.4360.6110.9091.0001.0000.9900.9090.9650.4440.6150.7040.6290.6290.6740.5740.6560.055
SS_Grade0.5260.2170.2800.2800.4410.5290.2830.6950.0680.5040.5280.1770.1940.1670.0810.1180.2690.2820.2800.2800.4260.1990.7570.2420.5760.2310.4060.6020.2440.9860.6530.6530.5100.9860.3290.5010.2910.9880.9980.9901.0000.9880.9000.2980.3120.7140.6240.5560.4930.5970.3730.000
SS_Status0.1360.0000.3350.3350.7740.7000.2900.7060.0000.7010.7010.0000.0400.3030.0000.0630.2920.2580.4570.4570.8170.3220.7260.1900.7210.1620.7080.7250.2900.9980.7090.7090.7080.9980.1260.1750.0051.0000.9900.9090.9881.0000.9860.3310.2390.7040.7660.7480.7610.7020.5800.000
SS_Total0.5280.4840.5500.5500.2880.2970.5560.083-0.0360.0410.041-0.0270.1990.2750.3030.3030.2810.0960.1800.1800.2530.1350.3910.4470.4580.4580.2130.3150.4491.0000.5360.5360.3311.0000.5530.5070.6250.9860.9650.9650.9000.9861.0000.4400.6340.1930.6450.6450.3480.1940.6660.029
Total_Credits_Earned0.8890.6310.6630.6630.3870.4260.6360.2290.0210.1770.1770.0080.3570.3120.3870.3680.3010.4420.2070.2070.2060.1230.6280.5870.5780.6000.4470.5150.5950.1450.5350.5350.3290.1450.5520.9360.7380.3310.4440.4440.2980.3310.4401.0000.7060.3140.4140.4140.3820.2700.4360.071
Total_GPC_Earned0.8210.8820.9270.9270.4460.4160.9360.1990.0500.1640.1640.0420.3520.3850.4490.4370.3870.3180.2290.2290.1710.1890.5950.8650.8770.8920.4410.4390.8880.1830.7680.7680.3940.1830.8060.8710.9910.2390.6150.6150.3120.2390.6340.7061.0000.2200.5540.5540.3640.2390.5880.049
UHV_Credit0.1780.1340.0000.0000.6930.7050.4040.7050.0000.7000.7000.0000.0000.0000.0000.0490.6920.2570.1090.1090.6860.0000.7050.0850.7090.1720.7070.6990.1980.9980.7050.7050.7030.9980.0730.1330.0000.7040.6980.7040.7140.7040.1930.3140.2201.0000.9960.9940.9900.9981.0000.000
UHV_GPA0.4600.4060.4460.4460.5490.5490.4780.7050.1400.2180.2180.1320.1290.0650.0960.1140.0720.2910.1770.1770.5860.1770.7610.4020.4070.3910.5670.5560.3950.9940.5270.5270.5650.9940.5440.4450.5460.7660.6290.6290.6240.7660.6450.4140.5540.9961.0001.0000.9940.8120.9520.096
UHV_GPC0.4810.4060.4460.4460.5200.5050.4780.7040.1400.2180.2180.1320.1640.0650.0960.1140.0720.2910.1770.1770.5120.1770.7460.4020.4070.3910.4900.5350.3950.9920.5270.5270.5100.9920.5440.4420.5460.7480.6290.6290.5560.7480.6450.4140.5540.9941.0001.0000.9960.8100.9520.096
UHV_Grade0.5110.2070.2370.2370.4460.5300.3240.7000.1030.4990.5020.1790.1600.0830.0000.0740.3840.3130.2640.2640.4510.2860.7560.1900.5580.2060.4420.5660.2670.9880.6420.6420.4500.9880.2720.4860.2810.7610.4840.6740.4930.7610.3480.3820.3640.9900.9940.9961.0000.8060.8440.247
UHV_Status0.1690.1140.1370.1370.5850.5720.2900.7050.1310.5780.5960.2120.0000.0000.0000.0000.5590.2500.0970.0970.5600.0000.7130.1600.5850.1450.5770.5780.2520.9960.5760.5760.5780.9960.0740.1640.0000.7020.5690.5740.5970.7020.1940.2700.2390.9980.8120.8100.8061.0000.0000.000
UHV_Total0.5060.4340.4800.4800.2660.3730.4980.0000.0730.1500.1500.0740.1240.1240.1600.1600.1380.1780.1910.1910.3130.1580.4360.4400.4290.4290.2140.3500.4241.0000.5380.5380.2781.0000.5660.5100.5810.5800.6560.6560.3730.5800.6660.4360.5881.0000.9520.9520.8440.0001.0000.106
UID0.0000.0500.0170.0170.0000.0000.0260.0000.0290.0690.0690.0430.000-0.0070.0150.0320.0010.0000.0100.0100.000-0.0550.0000.0200.0480.0310.0000.0000.0290.0000.0510.0510.0000.0000.0360.0000.0570.0000.0550.0550.0000.0000.0290.0710.0490.0000.0960.0960.2470.0000.1061.000

Missing values

2024-10-04T02:15:19.718683image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-04T02:15:19.989529image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-04T02:15:20.309321image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

UIDNameMaths_ESEMaths_TotalMaths_StatusMaths_GradeMaths_CreditMaths_GPAMaths_GPCBEE_ESEBEE_TotalBEE_StatusBEE_GradeBEE_CreditBEE_GPABEE_GPCEM_ESEEM_TotalEM_StatusEM_GradeEM_CreditEM_GPAEM_GPCEG_ESEEG_TotalEG_StatusEG_GradeEG_CreditEG_GPAEG_GPCSS_TotalSS_StatusSS_GradeSS_CreditSS_GPASS_GPCPSOOP_TotalPSOOP_StatusPSOOP_GradePSOOP_CreditPSOOP_GPAPSOOP_GPCUHV_TotalUHV_StatusUHV_GradeUHV_CreditUHV_GPAUHV_GPCLLC_TotalLLC_GradeLLC_CreditLLC_GPALLC_GPCTotal_CreditsTotal_Credits_EarnedTotal_GPC_EarnedSGPAResult
32023300001AARYAN MANTRI76.070.0passBB4832.064.062.0passBB483281.081passAB39270.00.00000086.0passAA2102088.0passAA3103077.0passBB281668.0BB18819.019.0165.08.680000Successful
42023300002AGARWAL VEDANT RAKESH93.091.0passAA41040.074.075.0passAB493662.079passAB39270.00.00000089.0passAA2102087.0passAA3103086.0passAA2102080.0AB19919.019.0182.09.580000Successful
52023300003KRISH PRAFULKUMAR AGRAWAL78.071.0passBB4832.054.063.0passBB483279.085passAA310300.00.00000067.0passBC271477.0passBB382477.0passBB281665.0BB18819.019.0156.08.210000Successful
72023300005ANGADI ARAN AJIT41.034.0passDD4416.09.024.0fail in ESEFF00011.046fail in ESEFF0000.00.00000075.0passBB281677.0passBB382472.0passBB281653.0CC16619.012.078.00.410526Unsuccessful
82023300006ATOLE ANIKET SAYAJI17.023.0fail in ESEFF000.014.037.0fail in ESEFF0000.000000025.056.0fail in ESEFF00058.0passCC261270.0passBB382469.0passBC271471.0AB19919.08.059.00.310526Unsuccessful
92023300007BADANI KOVIT DHARMESH47.058.0passCC4624.050.546.0passCC462440.062passBC37210.00.00000083.0passAB291879.0passAB392781.0passAB291882.0AB19919.019.0141.07.420000Successful
102023300008BAGADE ARJUN AJAY82.084.0passAA41040.077.075.0passAB493670.079passAB39270.00.00000082.0passAB291883.0passAB392782.0passAB291885.0AB19919.019.0175.09.210000Successful
112023300009BAGARIA HARSHIT SUNIL73.071.0passBB4832.040.044.0passCC462455.070passBB38240.00.00000080.0passAB291876.0passBB382475.0passBB281686.0AA1101019.019.0148.07.790000Successful
122023300010BALLA MAHADEV SHRIKRISHNA87.078.0passAB4936.061.070.0passBB48320.000000074.080.0passAB392778.0passAB291887.0passAA3103080.0passAB291890.0AA1101019.019.0171.09.000000Successful
132023300011BANGRE AANISH RAHUL73.075.0passBB4832.055.057.0passBC472844.065passBC37210.00.00000061.0passCC261280.0passAB392780.0passAB291895.0AA1101019.019.0148.07.790000Successful
UIDNameMaths_ESEMaths_TotalMaths_StatusMaths_GradeMaths_CreditMaths_GPAMaths_GPCBEE_ESEBEE_TotalBEE_StatusBEE_GradeBEE_CreditBEE_GPABEE_GPCEM_ESEEM_TotalEM_StatusEM_GradeEM_CreditEM_GPAEM_GPCEG_ESEEG_TotalEG_StatusEG_GradeEG_CreditEG_GPAEG_GPCSS_TotalSS_StatusSS_GradeSS_CreditSS_GPASS_GPCPSOOP_TotalPSOOP_StatusPSOOP_GradePSOOP_CreditPSOOP_GPAPSOOP_GPCUHV_TotalUHV_StatusUHV_GradeUHV_CreditUHV_GPAUHV_GPCLLC_TotalLLC_GradeLLC_CreditLLC_GPALLC_GPCTotal_CreditsTotal_Credits_EarnedTotal_GPC_EarnedSGPAResult
2622023300256VORA KALP CHIRAG4.011.0fail in ESEFF000.08.024.0fail in ESEFF0000.044absent in\rESEFF0000.00.00000067.0passBC271474.0passBB382473.0passBB281695.0AA1101019.08.064.00.336842Unsuccessful
2632023300257WADEKAR SUMIT ANIL82.080.0passAB4936.080.581.0passAA410400.000000074.074.0passBB382474.0passBB281687.0passAA3103079.0passAB291863.0BB18819.019.0172.09.050000Successful
2642023300258WADHWA KHUSHI JAGDISH76.079.0passAB4936.025.547.0passCC46240.000000042.061.0passBC372179.0passAB291875.0passBB382486.0passAA2102088.0AA1101019.019.0153.08.050000Successful
2652023300259WAGHMARE SAMYAK DHARMANAND42.049.0passCD4520.041.055.0passBC47280.000000049.056.0passCC361864.0passBC271476.0passBB382477.0passBB281667.0BB18819.019.0128.06.740000Successful
2662023300260WANKHEDE SWAPNIL RAJESH33.043.0passDD4416.042.047.0passCC462440.061passBC37210.00.00000065.0passBC271471.0passBB382473.0passBB281662.0BB18819.019.0123.06.470000Successful
2692023300263PRANAV PRADIP WASNIK40.043.0passDD4416.0NaN38.0absent in\rESENP0000.000000059.072.0passBB382453.0passCD251077.0passBB382468.0passBC271467.0BB18819.015.096.00.505263Unsuccessful
2702023300264YADAV VINAYAK CHANDRASHEKHAR66.0NaNBC472821.547.0NaNFF00000.0000003067.0NaNBB382468NaNBC271481NaNAB392786NaNAA2102036NaN000019.014.0113.00.594737Unsuccessful
2712023300265ZAVERI KRISHA TEJAS68.062.0passBC4728.049.052.0passCC462441.065passBC37210.00.00000085.0passAA2102080.0passAB392790.0passAA2102075.0AB19919.019.0149.07.840000Successful
2722023300266JAI PARAMESHWARAN77.082.0passAB4936.062.073.0passAB49360.000000068.092.0passAA3103089.0passAA2102085.0passAB392789.0passAA2102090.0AA1101019.019.0179.09.420000Successful
2732020300041Mesara Harshil73.079.0passAB4936.070.063.0passBB48320.000000044.067.0passBB382471.0passBB281684.0passAB392763.0passBC271480.0AB19919.019.0158.08.320000Successful